# Solve bug from mAP repo
!pip install matplotlib==3.5.3

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!pip install git+https://github.com/deepvision-class/starter-code

Collecting git+https://github.com/deepvision-class/starter-code
  Cloning https://github.com/deepvision-class/starter-code to /tmp/pip-req-build-neg_kcvo
  Running command git clone --filter=blob:none --quiet https://github.com/deepvision-class/starter-code /tmp/pip-req-build-neg_kcvo
  Resolved https://github.com/deepvision-class/starter-code to commit e8d9fe711870a39796a2f8ad95538e57942d756f
  Preparing metadata (setup.py) ... done
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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import coutils
from coutils import extract_drive_file_id, register_colab_notebooks, \
                    fix_random_seed, rel_error
import matplotlib.pyplot as plt
import numpy as np
import cv2
import copy
import time
import shutil
import os

# for plotting
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

# data type and device for torch.tensor
to_float = {'dtype': torch.float, 'device': 'cpu'}
to_float_cuda = {'dtype': torch.float, 'device': 'cuda'}
to_double = {'dtype': torch.double, 'device': 'cpu'}
to_double_cuda = {'dtype': torch.double, 'device': 'cuda'}
to_long = {'dtype': torch.long, 'device': 'cpu'}
to_long_cuda = {'dtype': torch.long, 'device': 'cuda'}

# for mAP evaluation
!rm -rf mAP
!git clone https://github.com/Cartucho/mAP.git
!rm -rf mAP/input/*

WARNING:root:pydrive is deprecated and no longer maintained. We recommend that you migrate your projects to pydrive2, the maintained fork of pydrive

Cloning into 'mAP'...
remote: Enumerating objects: 908, done.
remote: Counting objects: 100% (150/150), done.
remote: Compressing objects: 100% (25/25), done.
remote: Total 908 (delta 135), reused 125 (delta 125), pack-reused 758
Receiving objects: 100% (908/908), 14.70 MiB | 16.03 MiB/s, done.
Resolving deltas: 100% (329/329), done.

if torch.cuda.is_available:
  print('Good to go!')
else:
  print('Please set GPU via Edit -> Notebook Settings.')

Good to go!

def get_pascal_voc2007_data(image_root, split='train'):
  """
  Use torchvision.datasets
  https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.VOCDetection
  """
  from torchvision import datasets

  train_dataset = datasets.VOCDetection(image_root, year='2007', image_set=split,
                                    download=True)

  return train_dataset

# uncomment below to use the mirror link if the original link is broken
# !wget http://pjreddie.com/media/files/VOCtrainval_06-Nov-2007.tar
train_dataset = get_pascal_voc2007_data('/content', 'train')
val_dataset = get_pascal_voc2007_data('/content', 'val')

# an example on the raw annotation
import json
print(json.dumps(train_dataset[1][1]['annotation'], indent=2))

Downloading http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar to /content/VOCtrainval_06-Nov-2007.tar

100%|██████████| 460032000/460032000 [00:25<00:00, 18333771.33it/s]

Extracting /content/VOCtrainval_06-Nov-2007.tar to /content
Using downloaded and verified file: /content/VOCtrainval_06-Nov-2007.tar
Extracting /content/VOCtrainval_06-Nov-2007.tar to /content
{
  "folder": "VOC2007",
  "filename": "000017.jpg",
  "source": {
    "database": "The VOC2007 Database",
    "annotation": "PASCAL VOC2007",
    "image": "flickr",
    "flickrid": "228217974"
  },
  "owner": {
    "flickrid": "genewolf",
    "name": "whiskey kitten"
  },
  "size": {
    "width": "480",
    "height": "364",
    "depth": "3"
  },
  "segmented": "0",
  "object": [
    {
      "name": "person",
      "pose": "Left",
      "truncated": "0",
      "difficult": "0",
      "bndbox": {
        "xmin": "185",
        "ymin": "62",
        "xmax": "279",
        "ymax": "199"
      }
    },
    {
      "name": "horse",
      "pose": "Left",
      "truncated": "0",
      "difficult": "0",
      "bndbox": {
        "xmin": "90",
        "ymin": "78",
        "xmax": "403",
        "ymax": "336"
      }
    }
  ]
}

def pascal_voc2007_loader(dataset, batch_size, num_workers=0):
  """
  Data loader for Pascal VOC 2007.
  https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
  """
  from torch.utils.data import DataLoader
  # turn off shuffle so we can index the original image
  train_loader = DataLoader(dataset,
                            batch_size=batch_size,
                            shuffle=False, pin_memory=True,
                            num_workers=num_workers,
                            collate_fn=voc_collate_fn)
  return train_loader


class_to_idx = {'aeroplane':0, 'bicycle':1, 'bird':2, 'boat':3, 'bottle':4,
                'bus':5, 'car':6, 'cat':7, 'chair':8, 'cow':9, 'diningtable':10,
                'dog':11, 'horse':12, 'motorbike':13, 'person':14, 'pottedplant':15,
                'sheep':16, 'sofa':17, 'train':18, 'tvmonitor':19
}
idx_to_class = {i:c for c, i in class_to_idx.items()}


from torchvision import transforms

def voc_collate_fn(batch_lst, reshape_size=224):
    preprocess = transforms.Compose([
      transforms.Resize((reshape_size, reshape_size)),
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
      ])

    batch_size = len(batch_lst)

    img_batch = torch.zeros(batch_size, 3, reshape_size, reshape_size)

    max_num_box = max(len(batch_lst[i][1]['annotation']['object']) \
                      for i in range(batch_size))

    box_batch = torch.Tensor(batch_size, max_num_box, 5).fill_(-1.)
    w_list = []
    h_list = []
    img_id_list = []

    for i in range(batch_size):
      img, ann = batch_lst[i]
      w_list.append(img.size[0]) # image width
      h_list.append(img.size[1]) # image height
      img_id_list.append(ann['annotation']['filename'])
      img_batch[i] = preprocess(img)
      all_bbox = ann['annotation']['object']
      if type(all_bbox) == dict: # inconsistency in the annotation file
        all_bbox = [all_bbox]
      for bbox_idx, one_bbox in enumerate(all_bbox):
        bbox = one_bbox['bndbox']
        obj_cls = one_bbox['name']
        box_batch[i][bbox_idx] = torch.Tensor([float(bbox['xmin']), float(bbox['ymin']),
          float(bbox['xmax']), float(bbox['ymax']), class_to_idx[obj_cls]])

    h_batch = torch.tensor(h_list)
    w_batch = torch.tensor(w_list)

    return img_batch, box_batch, w_batch, h_batch, img_id_list

train_dataset = torch.utils.data.Subset(train_dataset, torch.arange(0, 2500)) # use 2500 samples for training
train_loader = pascal_voc2007_loader(train_dataset, 10)
val_loader = pascal_voc2007_loader(val_dataset, 10)

train_loader_iter = iter(train_loader)
img, ann, _, _, _ = next(train_loader_iter)

print('img has shape: ', img.shape)
print('ann has shape: ', ann.shape)

print('Image 1 has only two annotated objects, so ann[1] is padded with -1:')
print(ann[1])

print('\nImage 2 has six annotated objects:, so ann[2] is not padded:')
print(ann[2])

print('\nEach row in the annotation tensor indicates (x_tl, y_tl, x_br, y_br, class).')

img has shape:  torch.Size([10, 3, 224, 224])
ann has shape:  torch.Size([10, 6, 5])
Image 1 has only two annotated objects, so ann[1] is padded with -1:
tensor([[185.,  62., 279., 199.,  14.],
        [ 90.,  78., 403., 336.,  12.],
        [ -1.,  -1.,  -1.,  -1.,  -1.],
        [ -1.,  -1.,  -1.,  -1.,  -1.],
        [ -1.,  -1.,  -1.,  -1.,  -1.],
        [ -1.,  -1.,  -1.,  -1.,  -1.]])

Image 2 has six annotated objects:, so ann[2] is not padded:
tensor([[  9., 230., 245., 500.,   1.],
        [230., 220., 334., 500.,   1.],
        [  2., 178.,  90., 500.,   1.],
        [  2.,   1., 117., 369.,  14.],
        [  3.,   2., 243., 462.,  14.],
        [225.,   1., 334., 486.,  14.]])

Each row in the annotation tensor indicates (x_tl, y_tl, x_br, y_br, class).

def coord_trans(bbox, w_pixel, h_pixel, w_amap=7, h_amap=7, mode='a2p'):
  """
  Coordinate transformation function. It converts the box coordinate from
  the image coordinate system to the activation map coordinate system and vice versa.
  In our case, the input image will have a few hundred of pixels in
  width/height while the activation map is of size 7x7.

  Input:
  - bbox: Could be either bbox, anchor, or proposal, of shape Bx*x4
  - w_pixel: Number of pixels in the width side of the original image, of shape B
  - h_pixel: Number of pixels in the height side of the original image, of shape B
  - w_amap: Number of pixels in the width side of the activation map, scalar
  - h_amap: Number of pixels in the height side of the activation map, scalar
  - mode: Whether transfer from the original image to activation map ('p2a') or
          the opposite ('a2p')

  Output:
  - resized_bbox: Resized box coordinates, of the same shape as the input bbox
  """

  assert mode in ('p2a', 'a2p'), 'invalid coordinate transformation mode!'
  assert bbox.shape[-1] >= 4, 'the transformation is applied to the first 4 values of dim -1'

  if bbox.shape[0] == 0: # corner cases
    return bbox

  resized_bbox = bbox.clone()
  # could still work if the first dim of bbox is not batch size
  # in that case, w_pixel and h_pixel will be scalars
  resized_bbox = resized_bbox.view(bbox.shape[0], -1, bbox.shape[-1])
  invalid_bbox_mask = (resized_bbox == -1) # indicating invalid bbox

  if mode == 'p2a':
    # pixel to activation
    width_ratio = w_pixel * 1. / w_amap
    height_ratio = h_pixel * 1. / h_amap
    resized_bbox[:, :, [0, 2]] /= width_ratio.view(-1, 1, 1)
    resized_bbox[:, :, [1, 3]] /= height_ratio.view(-1, 1, 1)
  else:
    # activation to pixel
    width_ratio = w_pixel * 1. / w_amap
    height_ratio = h_pixel * 1. / h_amap
    resized_bbox[:, :, [0, 2]] *= width_ratio.view(-1, 1, 1)
    resized_bbox[:, :, [1, 3]] *= height_ratio.view(-1, 1, 1)

  resized_bbox.masked_fill_(invalid_bbox_mask, -1)
  resized_bbox.resize_as_(bbox)
  return resized_bbox

def data_visualizer(img, idx_to_class, bbox=None, pred=None):
  """
  Data visualizer on the original image. Support both GT box input and proposal input.

  Input:
  - img: PIL Image input
  - idx_to_class: Mapping from the index (0-19) to the class name
  - bbox: GT bbox (in red, optional), a tensor of shape Nx5, where N is
          the number of GT boxes, 5 indicates (x_tl, y_tl, x_br, y_br, class)
  - pred: Predicted bbox (in green, optional), a tensor of shape N'x6, where
          N' is the number of predicted boxes, 6 indicates
          (x_tl, y_tl, x_br, y_br, class, object confidence score)
  """

  img_copy = np.array(img).astype('uint8')

  if bbox is not None:
    for bbox_idx in range(bbox.shape[0]):
      one_bbox = bbox[bbox_idx][:4]
      top_left = (int(one_bbox[0]), int(one_bbox[1]))
      bottom_right = (int(one_bbox[2]), int(one_bbox[3]))
      cv2.rectangle(img_copy, top_left, bottom_right, (255, 0, 0), 2)
      if bbox.shape[1] > 4: # if class info provided
        obj_cls = idx_to_class[bbox[bbox_idx][4].item()]
        cv2.putText(img_copy, '%s' % (obj_cls),
                  (int(one_bbox[0]), int(one_bbox[1]+15)),
                  cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), thickness=1)

  if pred is not None:
    for bbox_idx in range(pred.shape[0]):
      one_bbox = pred[bbox_idx][:4]
      top_left = (int(one_bbox[0]), int(one_bbox[1]))
      bottom_right = (int(one_bbox[2]), int(one_bbox[3]))
      cv2.rectangle(img_copy, top_left, bottom_right, (0, 255, 0), 2)

      if pred.shape[1] > 4: # if class and conf score info provided
        obj_cls = idx_to_class[pred[bbox_idx][4].item()]
        conf_score = pred[bbox_idx][5].item()
        location = (int(one_bbox[0]), int(one_bbox[1]+15))
        cv2.putText(img_copy, '%s, %.2f' % (obj_cls, conf_score), location,
                    cv2.FONT_HERSHEY_PLAIN, 1.0, (0, 0, 255), thickness=1)

  plt.imshow(img_copy)
  plt.axis('off')
  plt.show()

# default examples for visualization
fix_random_seed(0)
batch_size = 3
sampled_idx = torch.linspace(0, len(train_dataset)-1, steps=batch_size).long()

# get the size of each image first
h_list = []
w_list = []
img_list = [] # list of images
MAX_NUM_BBOX = 40
box_list = torch.LongTensor(batch_size, MAX_NUM_BBOX, 5).fill_(-1) # PADDED GT boxes

for idx, i in enumerate(sampled_idx):
  # hack to get the original image so we don't have to load from local again...
  img, ann = train_dataset.__getitem__(i)
  img_list.append(img)

  all_bbox = ann['annotation']['object']
  if type(all_bbox) == dict:
    all_bbox = [all_bbox]
  for bbox_idx, one_bbox in enumerate(all_bbox):
    bbox = one_bbox['bndbox']
    obj_cls = one_bbox['name']
    box_list[idx][bbox_idx] = torch.LongTensor([int(bbox['xmin']), int(bbox['ymin']),
      int(bbox['xmax']), int(bbox['ymax']), class_to_idx[obj_cls]])

  # get sizes
  img = np.array(img)
  w_list.append(img.shape[1])
  h_list.append(img.shape[0])

w_list = torch.as_tensor(w_list, **to_float_cuda)
h_list = torch.as_tensor(h_list, **to_float_cuda)
box_list = torch.as_tensor(box_list, **to_float_cuda)
resized_box_list = coord_trans(box_list, w_list, h_list, mode='p2a') # on activation map coordinate system

# visualize GT boxes
for i in range(len(img_list)):
  valid_box = sum([1 if j != -1 else 0 for j in box_list[i][:, 0]])
  data_visualizer(img_list[i], idx_to_class, box_list[i][:valid_box])

class FeatureExtractor(nn.Module):
  """
  Image feature extraction with MobileNet.
  """
  def __init__(self, reshape_size=224, pooling=False, verbose=False):
    super().__init__()

    from torchvision import models
    from torchsummary import summary

    self.mobilenet = models.mobilenet_v2(pretrained=True)
    self.mobilenet = nn.Sequential(*list(self.mobilenet.children())[:-1]) # Remove the last classifier

    # average pooling
    if pooling:
      self.mobilenet.add_module('LastAvgPool', nn.AvgPool2d(math.ceil(reshape_size/32.))) # input: N x 1280 x 7 x 7

    for i in self.mobilenet.named_parameters():
      i[1].requires_grad = True # fine-tune all

    if verbose:
      summary(self.mobilenet.cuda(), (3, reshape_size, reshape_size))

  def forward(self, img, verbose=False):
    """
    Inputs:
    - img: Batch of resized images, of shape Nx3x224x224

    Outputs:
    - feat: Image feature, of shape Nx1280 (pooled) or Nx1280x7x7
    """
    num_img = img.shape[0]

    img_prepro = img

    feat = []
    process_batch = 500
    for b in range(math.ceil(num_img/process_batch)):
      feat.append(self.mobilenet(img_prepro[b*process_batch:(b+1)*process_batch]
                              ).squeeze(-1).squeeze(-1)) # forward and squeeze
    feat = torch.cat(feat)

    if verbose:
      print('Output feature shape: ', feat.shape)

    return feat

model = FeatureExtractor(verbose=True)

/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.
  warnings.warn(msg)
Downloading: "https://download.pytorch.org/models/mobilenet_v2-b0353104.pth" to /root/.cache/torch/hub/checkpoints/mobilenet_v2-b0353104.pth
100%|██████████| 13.6M/13.6M [00:00<00:00, 34.2MB/s]

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 32, 112, 112]             864
       BatchNorm2d-2         [-1, 32, 112, 112]              64
             ReLU6-3         [-1, 32, 112, 112]               0
            Conv2d-4         [-1, 32, 112, 112]             288
       BatchNorm2d-5         [-1, 32, 112, 112]              64
             ReLU6-6         [-1, 32, 112, 112]               0
            Conv2d-7         [-1, 16, 112, 112]             512
       BatchNorm2d-8         [-1, 16, 112, 112]              32
  InvertedResidual-9         [-1, 16, 112, 112]               0
           Conv2d-10         [-1, 96, 112, 112]           1,536
      BatchNorm2d-11         [-1, 96, 112, 112]             192
            ReLU6-12         [-1, 96, 112, 112]               0
           Conv2d-13           [-1, 96, 56, 56]             864
      BatchNorm2d-14           [-1, 96, 56, 56]             192
            ReLU6-15           [-1, 96, 56, 56]               0
           Conv2d-16           [-1, 24, 56, 56]           2,304
      BatchNorm2d-17           [-1, 24, 56, 56]              48
 InvertedResidual-18           [-1, 24, 56, 56]               0
           Conv2d-19          [-1, 144, 56, 56]           3,456
      BatchNorm2d-20          [-1, 144, 56, 56]             288
            ReLU6-21          [-1, 144, 56, 56]               0
           Conv2d-22          [-1, 144, 56, 56]           1,296
      BatchNorm2d-23          [-1, 144, 56, 56]             288
            ReLU6-24          [-1, 144, 56, 56]               0
           Conv2d-25           [-1, 24, 56, 56]           3,456
      BatchNorm2d-26           [-1, 24, 56, 56]              48
 InvertedResidual-27           [-1, 24, 56, 56]               0
           Conv2d-28          [-1, 144, 56, 56]           3,456
      BatchNorm2d-29          [-1, 144, 56, 56]             288
            ReLU6-30          [-1, 144, 56, 56]               0
           Conv2d-31          [-1, 144, 28, 28]           1,296
      BatchNorm2d-32          [-1, 144, 28, 28]             288
            ReLU6-33          [-1, 144, 28, 28]               0
           Conv2d-34           [-1, 32, 28, 28]           4,608
      BatchNorm2d-35           [-1, 32, 28, 28]              64
 InvertedResidual-36           [-1, 32, 28, 28]               0
           Conv2d-37          [-1, 192, 28, 28]           6,144
      BatchNorm2d-38          [-1, 192, 28, 28]             384
            ReLU6-39          [-1, 192, 28, 28]               0
           Conv2d-40          [-1, 192, 28, 28]           1,728
      BatchNorm2d-41          [-1, 192, 28, 28]             384
            ReLU6-42          [-1, 192, 28, 28]               0
           Conv2d-43           [-1, 32, 28, 28]           6,144
      BatchNorm2d-44           [-1, 32, 28, 28]              64
 InvertedResidual-45           [-1, 32, 28, 28]               0
           Conv2d-46          [-1, 192, 28, 28]           6,144
      BatchNorm2d-47          [-1, 192, 28, 28]             384
            ReLU6-48          [-1, 192, 28, 28]               0
           Conv2d-49          [-1, 192, 28, 28]           1,728
      BatchNorm2d-50          [-1, 192, 28, 28]             384
            ReLU6-51          [-1, 192, 28, 28]               0
           Conv2d-52           [-1, 32, 28, 28]           6,144
      BatchNorm2d-53           [-1, 32, 28, 28]              64
 InvertedResidual-54           [-1, 32, 28, 28]               0
           Conv2d-55          [-1, 192, 28, 28]           6,144
      BatchNorm2d-56          [-1, 192, 28, 28]             384
            ReLU6-57          [-1, 192, 28, 28]               0
           Conv2d-58          [-1, 192, 14, 14]           1,728
      BatchNorm2d-59          [-1, 192, 14, 14]             384
            ReLU6-60          [-1, 192, 14, 14]               0
           Conv2d-61           [-1, 64, 14, 14]          12,288
      BatchNorm2d-62           [-1, 64, 14, 14]             128
 InvertedResidual-63           [-1, 64, 14, 14]               0
           Conv2d-64          [-1, 384, 14, 14]          24,576
      BatchNorm2d-65          [-1, 384, 14, 14]             768
            ReLU6-66          [-1, 384, 14, 14]               0
           Conv2d-67          [-1, 384, 14, 14]           3,456
      BatchNorm2d-68          [-1, 384, 14, 14]             768
            ReLU6-69          [-1, 384, 14, 14]               0
           Conv2d-70           [-1, 64, 14, 14]          24,576
      BatchNorm2d-71           [-1, 64, 14, 14]             128
 InvertedResidual-72           [-1, 64, 14, 14]               0
           Conv2d-73          [-1, 384, 14, 14]          24,576
      BatchNorm2d-74          [-1, 384, 14, 14]             768
            ReLU6-75          [-1, 384, 14, 14]               0
           Conv2d-76          [-1, 384, 14, 14]           3,456
      BatchNorm2d-77          [-1, 384, 14, 14]             768
            ReLU6-78          [-1, 384, 14, 14]               0
           Conv2d-79           [-1, 64, 14, 14]          24,576
      BatchNorm2d-80           [-1, 64, 14, 14]             128
 InvertedResidual-81           [-1, 64, 14, 14]               0
           Conv2d-82          [-1, 384, 14, 14]          24,576
      BatchNorm2d-83          [-1, 384, 14, 14]             768
            ReLU6-84          [-1, 384, 14, 14]               0
           Conv2d-85          [-1, 384, 14, 14]           3,456
      BatchNorm2d-86          [-1, 384, 14, 14]             768
            ReLU6-87          [-1, 384, 14, 14]               0
           Conv2d-88           [-1, 64, 14, 14]          24,576
      BatchNorm2d-89           [-1, 64, 14, 14]             128
 InvertedResidual-90           [-1, 64, 14, 14]               0
           Conv2d-91          [-1, 384, 14, 14]          24,576
      BatchNorm2d-92          [-1, 384, 14, 14]             768
            ReLU6-93          [-1, 384, 14, 14]               0
           Conv2d-94          [-1, 384, 14, 14]           3,456
      BatchNorm2d-95          [-1, 384, 14, 14]             768
            ReLU6-96          [-1, 384, 14, 14]               0
           Conv2d-97           [-1, 96, 14, 14]          36,864
      BatchNorm2d-98           [-1, 96, 14, 14]             192
 InvertedResidual-99           [-1, 96, 14, 14]               0
          Conv2d-100          [-1, 576, 14, 14]          55,296
     BatchNorm2d-101          [-1, 576, 14, 14]           1,152
           ReLU6-102          [-1, 576, 14, 14]               0
          Conv2d-103          [-1, 576, 14, 14]           5,184
     BatchNorm2d-104          [-1, 576, 14, 14]           1,152
           ReLU6-105          [-1, 576, 14, 14]               0
          Conv2d-106           [-1, 96, 14, 14]          55,296
     BatchNorm2d-107           [-1, 96, 14, 14]             192
InvertedResidual-108           [-1, 96, 14, 14]               0
          Conv2d-109          [-1, 576, 14, 14]          55,296
     BatchNorm2d-110          [-1, 576, 14, 14]           1,152
           ReLU6-111          [-1, 576, 14, 14]               0
          Conv2d-112          [-1, 576, 14, 14]           5,184
     BatchNorm2d-113          [-1, 576, 14, 14]           1,152
           ReLU6-114          [-1, 576, 14, 14]               0
          Conv2d-115           [-1, 96, 14, 14]          55,296
     BatchNorm2d-116           [-1, 96, 14, 14]             192
InvertedResidual-117           [-1, 96, 14, 14]               0
          Conv2d-118          [-1, 576, 14, 14]          55,296
     BatchNorm2d-119          [-1, 576, 14, 14]           1,152
           ReLU6-120          [-1, 576, 14, 14]               0
          Conv2d-121            [-1, 576, 7, 7]           5,184
     BatchNorm2d-122            [-1, 576, 7, 7]           1,152
           ReLU6-123            [-1, 576, 7, 7]               0
          Conv2d-124            [-1, 160, 7, 7]          92,160
     BatchNorm2d-125            [-1, 160, 7, 7]             320
InvertedResidual-126            [-1, 160, 7, 7]               0
          Conv2d-127            [-1, 960, 7, 7]         153,600
     BatchNorm2d-128            [-1, 960, 7, 7]           1,920
           ReLU6-129            [-1, 960, 7, 7]               0
          Conv2d-130            [-1, 960, 7, 7]           8,640
     BatchNorm2d-131            [-1, 960, 7, 7]           1,920
           ReLU6-132            [-1, 960, 7, 7]               0
          Conv2d-133            [-1, 160, 7, 7]         153,600
     BatchNorm2d-134            [-1, 160, 7, 7]             320
InvertedResidual-135            [-1, 160, 7, 7]               0
          Conv2d-136            [-1, 960, 7, 7]         153,600
     BatchNorm2d-137            [-1, 960, 7, 7]           1,920
           ReLU6-138            [-1, 960, 7, 7]               0
          Conv2d-139            [-1, 960, 7, 7]           8,640
     BatchNorm2d-140            [-1, 960, 7, 7]           1,920
           ReLU6-141            [-1, 960, 7, 7]               0
          Conv2d-142            [-1, 160, 7, 7]         153,600
     BatchNorm2d-143            [-1, 160, 7, 7]             320
InvertedResidual-144            [-1, 160, 7, 7]               0
          Conv2d-145            [-1, 960, 7, 7]         153,600
     BatchNorm2d-146            [-1, 960, 7, 7]           1,920
           ReLU6-147            [-1, 960, 7, 7]               0
          Conv2d-148            [-1, 960, 7, 7]           8,640
     BatchNorm2d-149            [-1, 960, 7, 7]           1,920
           ReLU6-150            [-1, 960, 7, 7]               0
          Conv2d-151            [-1, 320, 7, 7]         307,200
     BatchNorm2d-152            [-1, 320, 7, 7]             640
InvertedResidual-153            [-1, 320, 7, 7]               0
          Conv2d-154           [-1, 1280, 7, 7]         409,600
     BatchNorm2d-155           [-1, 1280, 7, 7]           2,560
           ReLU6-156           [-1, 1280, 7, 7]               0
================================================================
Total params: 2,223,872
Trainable params: 2,223,872
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 152.85
Params size (MB): 8.48
Estimated Total Size (MB): 161.91
----------------------------------------------------------------

# Declare variables for anchor priors, a Ax2 Tensor where A is the number of anchors.
# Hand-picked, same as our two-stage detector.
anchor_list = torch.tensor([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [2, 3], [3, 2], [3, 5], [5, 3]], **to_float_cuda)
print(anchor_list.shape)

torch.Size([9, 2])

def GenerateGrid(batch_size, w_amap=7, h_amap=7, dtype=torch.float32, device='cuda'):
  """
  Return a grid cell given a batch size (center coordinates).

  Inputs:
  - batch_size, B
  - w_amap: or W', width of the activation map (number of grids in the horizontal dimension)
  - h_amap: or H', height of the activation map (number of grids in the vertical dimension)
  - W' and H' are always 7 in our case while w and h might vary.

  Outputs:
  grid: A float32 tensor of shape (B, H', W', 2) giving the (x, y) coordinates
        of the centers of each feature for a feature map of shape (B, D, H', W')
  """
  w_range = torch.arange(0, w_amap, dtype=dtype, device=device) + 0.5
  h_range = torch.arange(0, h_amap, dtype=dtype, device=device) + 0.5

  w_grid_idx = w_range.unsqueeze(0).repeat(h_amap, 1)
  h_grid_idx = h_range.unsqueeze(1).repeat(1, w_amap)
  grid = torch.stack([w_grid_idx, h_grid_idx], dim=-1)
  grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1)

  return grid

# visualization
# simply create an activation grid where the cells are in green and the centers in red
# you should see the entire image divided by a 7x7 grid, with no gaps on the edges

grid_list = GenerateGrid(w_list.shape[0], device='cuda')

center = torch.cat((grid_list, grid_list), dim=-1)
grid_cell = center.clone()
grid_cell[:, :, :, [0, 1]] -= 1. / 2.
grid_cell[:, :, :, [2, 3]] += 1. / 2.
center = coord_trans(center, w_list, h_list)
grid_cell = coord_trans(grid_cell, w_list, h_list)

for img, anc, grid in zip(img_list, center, grid_cell):
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4), grid.reshape(-1, 4))

def GenerateAnchor(anc, grid):
    """
     Anchor generator.

    Inputs:
    - anc: Tensor of shape (A, 2) giving the shapes of anchor boxes to consider at
      each point in the grid. anc[a] = (w, h) gives the width and height of the
      a'th anchor shape.
    - grid: Tensor of shape (B, H', W', 2) giving the (x, y) coordinates of the
      center of each feature from the backbone feature map. This is the tensor
      returned from GenerateGrid.

    Outputs:
    - anchors: Tensor of shape (B, A, H', W', 4) giving the positions of all
      anchor boxes for the entire image. anchors[b, a, h, w] is an anchor box
      centered at grid[b, h, w], whose shape is given by anc[a]; we parameterize
      boxes as anchors[b, a, h, w] = (x_tl, y_tl, x_br, y_br), where (x_tl, y_tl)
      and (x_br, y_br) give the xy coordinates of the top-left and bottom-right
      corners of the box.
    """
    anchors = None
    ##############################################################################
    # TODO: Given a set of anchor shapes and a grid cell on the activation map,  #
    # generate all the anchor coordinates for each image. Support batch input.   #
    ##############################################################################

    anchors = torch.Tensor(grid.shape[0], anc.shape[0], grid.shape[1], grid.shape[2], 4).fill_(-1.).cuda()

    anc_temp = anc[:, None, None, :]    # Want    (A, 1,  1,  2)
    grid_temp = grid[:, None, :, :, :]  # Want (B, 1, H', W', 2)

    anchors[..., 0] = grid_temp[..., 0] - anc_temp[..., 0] / 2
    anchors[..., 2] = grid_temp[..., 0] + anc_temp[..., 0] / 2
    anchors[..., 1] = grid_temp[..., 1] - anc_temp[..., 1] / 2
    anchors[..., 3] = grid_temp[..., 1] + anc_temp[..., 1] / 2

    ##############################################################################
    #                               END OF YOUR CODE                             #
    ##############################################################################

    return anchors

# sanity check
anc_list = GenerateAnchor(anchor_list, grid_list)
assert anc_list.shape == torch.Size([3, 9, 7, 7, 4]), 'shape mismatch!'

expected_anc_list_mean = torch.tensor([[-1.25000000, -0.87500000,  2.25000000,  1.87500000],
                                       [ 1.75000000, -0.87500000,  5.25000000,  1.87500000],
                                       [ 4.75000000, -0.87500000,  8.25000000,  1.87500000],
                                       [-1.25000000,  1.12500000,  2.25000000,  3.87500000],
                                       [ 1.75000000,  1.12500000,  5.25000000,  3.87500000],
                                       [ 4.75000000,  1.12500000,  8.25000000,  3.87500000],
                                       [-1.25000000,  3.12500000,  2.25000000,  5.87500000],
                                       [ 1.75000000,  3.12500000,  5.25000000,  5.87500000],
                                       [ 4.75000000,  3.12500000,  8.25000000,  5.87500000],
                                       [-1.25000000,  5.12500000,  2.25000000,  7.87500000],
                                       [ 1.75000000,  5.12500000,  5.25000000,  7.87500000],
                                       [ 4.75000000,  5.12500000,  8.25000000,  7.87500000]], **to_float_cuda)
error = rel_error(expected_anc_list_mean, anc_list[0, [1,3,6,8], ::2, ::3, :].view(-1, 12, 4).mean(0))
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Relative Error: {error}\n{color}Result: {result}\033[0m')

Relative Error: 0.0
Result: Pass

# visualization
print('*'*80)
print('All nine anchors should be exactly centered:')
anc_list = GenerateAnchor(anchor_list, grid_list[:, 3:4, 3:4])
for img, anc in zip(img_list, coord_trans(anc_list, w_list, h_list)):
  print(anc.shape)
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4))

print('*'*80)
print('All anchors of the image (cluttered):')
anc_list = GenerateAnchor(anchor_list, grid_list) # all
for img, anc in zip(img_list, coord_trans(anc_list, w_list, h_list)):
  print(anc.shape)
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4))

********************************************************************************
All nine anchors should be exactly centered:
torch.Size([9, 1, 1, 4])

torch.Size([9, 1, 1, 4])

torch.Size([9, 1, 1, 4])

********************************************************************************
All anchors of the image (cluttered):
torch.Size([9, 7, 7, 4])

torch.Size([9, 7, 7, 4])

torch.Size([9, 7, 7, 4])

def GenerateProposal(anchors, offsets, method='YOLO'):
    """
    Proposal generator.

    Inputs:
    - anchors: Anchor boxes, of shape (B, A, H', W', 4). Anchors are represented
      by the coordinates of their top-left and bottom-right corners.
    - offsets: Transformations of shape (B, A, H', W', 4) that will be used to
      convert anchor boxes into region proposals. The transformation
      offsets[b, a, h, w] = (tx, ty, tw, th) will be applied to the anchor
      anchors[b, a, h, w]. For YOLO, assume that tx and ty are in the range
      (-0.5, 0.5).
    - method: Which transformation formula to use, either 'YOLO' or 'FasterRCNN'

    Outputs:
    - proposals: Region proposals of shape (B, A, H', W', 4), represented by the
      coordinates of their top-left and bottom-right corners. Applying the
      transform offsets[b, a, h, w] to the anchor [b, a, h, w] should give the
      proposal proposals[b, a, h, w].

    """
    assert(method in ['YOLO', 'FasterRCNN'])
    proposals = None
    ##############################################################################
    # TODO: Given anchor coordinates and the proposed offset for each anchor,    #
    # compute the proposal coordinates using the transformation formulas above.  #
    ##############################################################################

    coords = torch.Tensor(anchors.shape).fill_(-1.).cuda()
    proposals = torch.Tensor(anchors.shape).fill_(-1.).cuda()

    # Transform corner coordinates to center/width/height representation
    coords[..., 0] = (anchors[..., 0] + anchors[..., 2]) / 2
    coords[..., 1] = (anchors[..., 1] + anchors[..., 3]) / 2
    coords[..., 2] = anchors[..., 2] - anchors[..., 0]
    coords[..., 3] = anchors[..., 3] - anchors[..., 1]

    if method == 'YOLO':
        proposals[..., 0] = coords[..., 0] + offsets[..., 0]
        proposals[..., 1] = coords[..., 1] + offsets[..., 1]
        proposals[..., 2] = coords[..., 2] * torch.exp(offsets[..., 2])
        proposals[..., 3] = coords[..., 3] * torch.exp(offsets[..., 3])
    elif method == 'FasterRCNN':
        proposals[..., 0] = coords[..., 0] + offsets[..., 0] * coords[..., 2]
        proposals[..., 1] = coords[..., 1] + offsets[..., 1] * coords[..., 3]
        proposals[..., 2] = coords[..., 2] * torch.exp(offsets[..., 2])
        proposals[..., 3] = coords[..., 3] * torch.exp(offsets[..., 3])

    # Transform center/width/height representation back to corner coordinates
    coords[..., 0] = proposals[..., 0] - proposals[..., 2] / 2
    coords[..., 2] = proposals[..., 0] + proposals[..., 2] / 2
    coords[..., 1] = proposals[..., 1] - proposals[..., 3] / 2
    coords[..., 3] = proposals[..., 1] + proposals[..., 3] / 2
    proposals = coords  # Reassignment for return

    ##############################################################################
    #                               END OF YOUR CODE                             #
    ##############################################################################

    return proposals

print('-' * 80)
print('Sanity check for YOLO.')
yolo_offset_list = torch.cat([torch.ones_like(anc_list[:, :, :, :, 0:2]).fill_(.5), torch.ones_like(anc_list[:, :, :, :, 2:4])], dim=-1)
yolo_proposal_list = GenerateProposal(anc_list, yolo_offset_list, 'YOLO') # no scaling

print('1. Center moved by ~0.5 cell')
error = rel_error(anc_list[0, 0, 0, :, 0:2] + (anc_list[0, 0, 0, :, 2:4] - anc_list[0, 0, 0, :, 0:2])/2.0 + 0.5, \
        (yolo_proposal_list[0, 0, 0, :, 0:2] + (yolo_proposal_list[0, 0, 0, :, 2:4] - yolo_proposal_list[0, 0, 0, :, 0:2]) / 2.0))
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Relative Error: {error}\n{color}Result: {result}\033[0m')

print('2. w/h changed by e')
error = rel_error((anc_list[0, 0, 0, :, 2:4] - anc_list[0, 0, 0, :, 0:2]) * torch.exp(torch.ones_like(anc_list[0, 0, 0, :, 0:2])), \
        (yolo_proposal_list[0, 0, 0, :, 2:4] - yolo_proposal_list[0, 0, 0, :, 0:2]))
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Relative Error: {error}\n{color}Result: {result}\033[0m')

print('-' * 80)
print('Sanity check for FasterRCNN.')
rcnn_offset_list = torch.ones_like(anc_list)
rcnn_proposal_list = GenerateProposal(anc_list, rcnn_offset_list, 'FasterRCNN')

print('1. x/y shifted by wh')
error = rel_error(anc_list[0, 0, 0, :, 0:2] + (anc_list[0, 0, 0, :, 2:4] - anc_list[0, 0, 0, :, 0:2]) * 3.0 /2.0, \
      (rcnn_proposal_list[0, 0, 0, :, 0:2] + (rcnn_proposal_list[0, 0, 0, :, 2:4] - rcnn_proposal_list[0, 0, 0, :, 0:2]) / 2.0))
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Relative Error: {error}\n{color}Result: {result}\033[0m')

print('2. w/h should changed by e')
error = rel_error((anc_list[0, 0, 0, :, 2:4] - anc_list[0, 0, 0, :, 0:2]) * torch.exp(torch.ones_like(anc_list[0, 0, 0, :, 0:2])), \
        (rcnn_proposal_list[0, 0, 0, :, 2:4] - rcnn_proposal_list[0, 0, 0, :, 0:2]))
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Relative Error: {error}\n{color}Result: {result}\033[0m')

--------------------------------------------------------------------------------
Sanity check for YOLO.
1. Center moved by ~0.5 cell
Relative Error: 0.0
Result: Pass
2. w/h changed by e
Relative Error: 1.7541862007419695e-07
Result: Pass
--------------------------------------------------------------------------------
Sanity check for FasterRCNN.
1. x/y shifted by wh
Relative Error: 0.0
Result: Pass
2. w/h should changed by e
Relative Error: 1.7541862007419695e-07
Result: Pass

yolo_offset_list = torch.cat([torch.ones_like(anc_list[:, :, :, :, 0:2]).fill_(.5), torch.zeros_like(anc_list[:, :, :, :, 2:4])], dim=-1)
yolo_proposal_list = GenerateProposal(anc_list, yolo_offset_list, 'YOLO')

for img, anc, prop in zip(img_list, coord_trans(anc_list[:, 0:1, 3:4, 3:4, :], w_list, h_list), \
                          coord_trans(yolo_proposal_list[:, 0:1, 3:4, 3:4, :], w_list, h_list)):
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4), prop.reshape(-1, 4))

yolo_offset_list = torch.cat([torch.zeros_like(anc_list[:, :, :, :, 0:2]), torch.ones_like(anc_list[:, :, :, :, 2:4]).fill_(1.)], dim=-1)
yolo_proposal_list = GenerateProposal(anc_list, yolo_offset_list, 'YOLO')

for img, anc, prop in zip(img_list, coord_trans(anc_list[:, 0:1, 3:4, 3:4, :], w_list, h_list), \
                          coord_trans(yolo_proposal_list[:, 0:1, 3:4, 3:4, :], w_list, h_list)):
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4), prop.reshape(-1, 4))

# visualization (shift by wh, Faster R-CNN)
# anchors in red and proposals in green
rcnn_offset_list = torch.cat([torch.ones_like(anc_list[:, :, :, :, 0:2]), torch.zeros_like(anc_list[:, :, :, :, 2:4])], dim=-1)
rcnn_proposal_list = GenerateProposal(anc_list, rcnn_offset_list, 'FasterRCNN')

for img, anc, prop in zip(img_list, coord_trans(anc_list[:, 0:1, 3:4, 3:4, :], w_list, h_list), \
                          coord_trans(rcnn_proposal_list[:, 0:1, 3:4, 3:4, :], w_list, h_list)):
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4), prop.reshape(-1, 4))

# visualization (no shift and then scale by e, Faster R-CNN)
# anchors in red and proposals in green
rcnn_offset_list = torch.cat([torch.zeros_like(anc_list[:, :, :, :, 0:2]), torch.ones_like(anc_list[:, :, :, :, 2:4]).fill_(1)], dim=-1)
rcnn_proposal_list = GenerateProposal(anc_list, rcnn_offset_list, 'FasterRCNN')

for img, anc, prop in zip(img_list, coord_trans(anc_list[:, 0:1, 3:4, 3:4, :], w_list, h_list), \
                          coord_trans(rcnn_proposal_list[:, 0:1, 3:4, 3:4, :], w_list, h_list)):
  data_visualizer(img, idx_to_class, anc.reshape(-1, 4), prop.reshape(-1, 4))

def IoU(proposals, bboxes):
    """
    Compute intersection over union between sets of bounding boxes.

    Inputs:
    - proposals: Proposals of shape (B, A, H', W', 4)
    - bboxes: Ground-truth boxes from the DataLoader of shape (B, N, 5).
      Each ground-truth box is represented as tuple (x_lr, y_lr, x_rb, y_rb, class).
      If image i has fewer than N boxes, then bboxes[i] will be padded with extra
      rows of -1.

    Outputs:
    - iou_mat: IoU matrix of shape (B, A*H'*W', N) where iou_mat[b, i, n] gives
      the IoU between one element of proposals[b] and bboxes[b, n].

    For this implementation you DO NOT need to filter invalid proposals or boxes;
    in particular you don't need any special handling for bboxxes that are padded
    with -1.
    """
    iou_mat = None
    ##############################################################################
    # TODO: Compute the Intersection over Union (IoU) on proposals and GT boxes. #
    # No need to filter invalid proposals/bboxes (i.e., allow region area <= 0). #
    # You need to ensure your implementation is efficient (no for loops).        #
    # HINT:                                                                      #
    # IoU = Area of Intersection / Area of Union, where
    # Area of Union = Area of Proposal + Area of BBox - Area of Intersection     #
    # and the Area of Intersection can be computed using the top-left corner and #
    # bottom-right corner of proposal and bbox. Think about their relationships. #
    ##############################################################################

    # Extract the tensor sizes
    B, A, H_, W_, _ = proposals.shape
    N = bboxes.shape[1]

    # Make both proposals and bboxes have the same shape
    # Note: Ignore the class label in bboxes
    proposals = proposals.view(B, A*H_*W_, 1, 4).expand(-1, -1, N, -1)  # (B, AHW, N, 4)
    bboxes = bboxes[:, None, :, :4].expand(-1, A*H_*W_, -1, -1)         # (B, AHW, N, 5)

    # Find the inner corners for intersection
    # Remove negative intersections
    min_bottom_right = torch.min(proposals[..., 2:], bboxes[..., 2:])
    max_top_left = torch.max(proposals[..., :2], bboxes[..., :2])
    intersection = torch.clamp(min_bottom_right - max_top_left, min=0)
    intersection_area = intersection[..., 0] * intersection[..., 1]

    # Find the individual areas
    proposals_area = (proposals[..., 2] - proposals[..., 0]) * (proposals[..., 3] - proposals[..., 1])
    bboxes_area = (bboxes[..., 2] - bboxes[..., 0]) * (bboxes[..., 3] - bboxes[..., 1])

    # Calculate the union area and IoU
    union_area = proposals_area + bboxes_area - intersection_area
    iou_mat = intersection_area / union_area

    ##############################################################################
    #                               END OF YOUR CODE                             #
    ##############################################################################
    return iou_mat

# simple sanity check
width, height = torch.tensor([35, 35], **to_float_cuda), torch.tensor([40, 40], **to_float_cuda)
sample_bbox = torch.tensor([[[1,1,11,11,0], [20,20,30,30,0]]], **to_float_cuda)
sample_proposals = torch.tensor([[[[[5,5,15,15], [27,27,37,37]]]]], **to_float_cuda)

result = IoU(sample_proposals, sample_bbox)

# check 1
expected_result = torch.tensor([[[0.21951219, 0.00000000],
                                 [0.00000000, 0.04712042]]], **to_float_cuda)
error = rel_error(expected_result, result)
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Simple IoU Matrix Relative Error: {error}\n{color}Result: {result}\033[0m')

# check 2
iou_mat = IoU(anc_list, resized_box_list)
expected_iou_mat = torch.tensor([0.11666405, 0.15146968, 0.02956639], **to_float_cuda)
error = rel_error(expected_iou_mat, iou_mat[:, :, 0].mean(1))
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'IoU Matrix Relative Error: {error}\n{color}Result: {result}\033[0m')

Simple IoU Matrix Relative Error: 0.0
Result: Pass
IoU Matrix Relative Error: 6.299873689386004e-08
Result: Pass

def ReferenceOnActivatedAnchors(anchors, bboxes, grid, iou_mat, pos_thresh=0.7, neg_thresh=0.3, method='FasterRCNN'):
  """
  Determine the activated (positive) and negative anchors for model training.

  For YOLO - A grid cell is responsible for predicting a GT box if the center of
  the box falls into that cell.
  Implementation details: First compute manhattan distance between grid cell centers
  (BxH’xW’) and GT box centers (BxN). This gives us a matrix of shape Bx(H'xW')xN and
  perform torch.min(dim=1)[1] on it gives us the indexes indicating activated grids
  responsible for GT boxes (convert to x and y). Among all the anchors associated with
  the activate grids, the anchor with the largest IoU with the GT box is responsible to
  predict (regress to) the GT box.
  Note: One anchor might match multiple GT boxes.

  For Faster R-CNN - Positive anchors are defined Any of the two
  (i) the anchor/anchors with the highest IoU overlap with a GT box, or
  (ii) an anchor that has an IoU overlap higher than 0.7 with any GT box.
  Note: One anchor can match at most one GT box (the one with the largest IoU overlapping).

  For both - We assign a negative label to a anchor if its IoU ratio is lower than
  a threshold value for all GT boxes. Anchors that are neither positive nor negative
  do not contribute to the training objective.

  Main steps include:
  i) Decide activated and negative anchors based on the IoU matrix.
  ii) Compute GT confidence score/offsets/object class on the positive proposals.
  iii) Compute GT confidence score on the negative proposals.

  Inputs:
  - anchors: Anchor boxes, of shape BxAxH’xW’x4
  - bboxes: GT boxes of shape BxNx5, where N is the number of PADDED GT boxes,
            5 indicates (x_{lr}^{gt}, y_{lr}^{gt}, x_{rb}^{gt}, y_{rb}^{gt}) and class index
  - grid (float): A cell grid of shape BxH'xW'x2 where 2 indicate the (x, y) coord
  - iou_mat: IoU matrix of shape Bx(AxH’xW’)xN
  - pos_thresh: Positive threshold value
  - neg_thresh: Negative threshold value
  - method: Switch between 'YOLO' mode and 'FasterRCNN' mode

  Outputs:
  - activated_anc_ind: Index on activated anchors, of shape M, where M indicates the
                       number of activated anchors
  - negative_anc_ind: Index on negative anchors, of shape M
  - GT_conf_scores: GT IoU confidence scores on activated anchors, of shape M
  - GT_offsets: GT offsets on activated anchors, of shape Mx4. They are denoted as
                \hat{t^x}, \hat{t^y}, \hat{t^w}, \hat{t^h} in the formulation earlier.
  - GT_class: GT class category on activated anchors, essentially indexed from bboxes[:, :, 4],
              of shape M
  - activated_anc_coord: Coordinates on activated anchors (mainly for visualization purposes)
  - negative_anc_coord: Coordinates on negative anchors (mainly for visualization purposes)
  """

  assert(method in ['FasterRCNN', 'YOLO'])

  B, A, h_amap, w_amap, _ = anchors.shape
  N = bboxes.shape[1]

  # activated/positive anchors
  max_iou_per_anc, max_iou_per_anc_ind = iou_mat.max(dim=-1)
  if method == 'FasterRCNN':
    max_iou_per_box = iou_mat.max(dim=1, keepdim=True)[0]
    activated_anc_mask = (iou_mat == max_iou_per_box) & (max_iou_per_box > 0)
    activated_anc_mask |= (iou_mat > pos_thresh) # using the pos_thresh condition as well
    # if an anchor matches multiple GT boxes, choose the box with the largest iou
    activated_anc_mask = activated_anc_mask.max(dim=-1)[0] # Bx(AxH’xW’)
    activated_anc_ind = torch.nonzero(activated_anc_mask.view(-1)).squeeze(-1)

    # GT conf scores
    GT_conf_scores = max_iou_per_anc[activated_anc_mask] # M

    # GT class
    box_cls = bboxes[:, :, 4].view(B, 1, N).expand((B, A*h_amap*w_amap, N))
    GT_class = torch.gather(box_cls, -1, max_iou_per_anc_ind.unsqueeze(-1)).squeeze(-1) # M
    GT_class = GT_class[activated_anc_mask].long()

    bboxes_expand = bboxes[:, :, :4].view(B, 1, N, 4).expand((B, A*h_amap*w_amap, N, 4))
    bboxes = torch.gather(bboxes_expand, -2, max_iou_per_anc_ind.unsqueeze(-1) \
      .unsqueeze(-1).expand(B, A*h_amap*w_amap, 1, 4)).view(-1, 4)
    bboxes = bboxes[activated_anc_ind]
  else:
    bbox_mask = (bboxes[:, :, 0] != -1) # BxN, indicate invalid boxes
    bbox_centers = (bboxes[:, :, 2:4] - bboxes[:, :, :2]) / 2. + bboxes[:, :, :2] # BxNx2

    mah_dist = torch.abs(grid.view(B, -1, 2).unsqueeze(2) - bbox_centers.unsqueeze(1)).sum(dim=-1) # Bx(H'xW')xN
    min_mah_dist = mah_dist.min(dim=1, keepdim=True)[0] # Bx1xN
    grid_mask = (mah_dist == min_mah_dist).unsqueeze(1) # Bx1x(H'xW')xN

    reshaped_iou_mat = iou_mat.view(B, A, -1, N)
    anc_with_largest_iou = reshaped_iou_mat.max(dim=1, keepdim=True)[0] # Bx1x(H’xW’)xN
    anc_mask = (anc_with_largest_iou == reshaped_iou_mat) # BxAx(H’xW’)xN
    activated_anc_mask = (grid_mask & anc_mask).view(B, -1, N)
    activated_anc_mask &= bbox_mask.unsqueeze(1)

    # one anchor could match multiple GT boxes
    activated_anc_ind = torch.nonzero(activated_anc_mask.view(-1)).squeeze(-1)
    GT_conf_scores = iou_mat.view(-1)[activated_anc_ind]
    bboxes = bboxes.view(B, 1, N, 5).repeat(1, A*h_amap*w_amap, 1, 1).view(-1, 5)[activated_anc_ind]
    GT_class = bboxes[:, 4].long()
    bboxes = bboxes[:, :4]
    activated_anc_ind = (activated_anc_ind / activated_anc_mask.shape[-1]).long()

  print('number of pos proposals: ', activated_anc_ind.shape[0])
  activated_anc_coord = anchors.view(-1, 4)[activated_anc_ind]

  # GT offsets
  # bbox and anchor coordinates are x_tl, y_tl, x_br, y_br
  # offsets are t_x, t_y, t_w, t_h
  wh_offsets = torch.log((bboxes[:, 2:4] - bboxes[:, :2]) \
    / (activated_anc_coord[:, 2:4] - activated_anc_coord[:, :2]))

  xy_offsets = (bboxes[:, :2] + bboxes[:, 2:4] - \
    activated_anc_coord[:, :2] - activated_anc_coord[:, 2:4]) / 2.

  if method == "FasterRCNN":
    xy_offsets /= (activated_anc_coord[:, 2:4] - activated_anc_coord[:, :2])
  else:
    assert torch.max(torch.abs(xy_offsets)) <= 0.5, \
      "x and y offsets should be between -0.5 and 0.5! Got {}".format( \
      torch.max(torch.abs(xy_offsets)))

  GT_offsets = torch.cat((xy_offsets, wh_offsets), dim=-1)

  # negative anchors
  negative_anc_mask = (max_iou_per_anc < neg_thresh) # Bx(AxH’xW’)
  negative_anc_ind = torch.nonzero(negative_anc_mask.view(-1)).squeeze(-1)
  negative_anc_ind = negative_anc_ind[torch.randint(0, negative_anc_ind.shape[0], (activated_anc_ind.shape[0],))]
  negative_anc_coord = anchors.view(-1, 4)[negative_anc_ind.view(-1)]

  # activated_anc_coord and negative_anc_coord are mainly for visualization purposes
  return activated_anc_ind, negative_anc_ind, GT_conf_scores, GT_offsets, GT_class, \
         activated_anc_coord, negative_anc_coord

# sanity check on YOLO (the one on Faster R-CNN is in A5-2)
activated_anc_ind, negative_anc_ind, GT_conf_scores, GT_offsets, GT_class, \
  activated_anc_coord, negative_anc_coord = ReferenceOnActivatedAnchors(anc_list, resized_box_list, grid_list, iou_mat, neg_thresh=0.2, method='YOLO')

expected_GT_conf_scores = torch.tensor([0.74538743, 0.72793430, 0.76044953, 0.37116671], **to_float_cuda)
expected_GT_offsets = torch.tensor([[ 0.04900002,  0.35735703, -0.09431065,  0.19244696],
                                    [-0.14700007,  0.37299442, -0.00250307,  0.25213102],
                                    [-0.40600014,  0.09625626,  0.20863886, -0.07974572],
                                    [ 0.15399981, -0.42933345, -0.03459148, -0.86750042]], **to_float_cuda)
expected_GT_class = torch.tensor([ 6,  7, 19,  6], **to_long_cuda)

error = rel_error(GT_conf_scores, expected_GT_conf_scores)
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Confidence Scores Relative Error: {error}\n{color}Result: {result}\033[0m')

error = rel_error(GT_offsets, expected_GT_offsets)
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Offsets Relative Error: {error}\n{color}Result: {result}\033[0m')

error = rel_error(GT_class, expected_GT_class)
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Class Probabilities Relative Error: {error}\n{color}Result: {result}\033[0m')

number of pos proposals:  4
Confidence Scores Relative Error: 0.0
Result: Pass
Offsets Relative Error: 7.441442448907765e-07
Result: Pass
Class Probabilities Relative Error: 0.0
Result: Pass

# visualize activated and negative anchors
anc_per_img = torch.prod(torch.tensor(anc_list.shape[1:-1]))

print('*'*80)
print('Activated (positive) anchors:')
for img, bbox, idx in zip(img_list, box_list, torch.arange(box_list.shape[0])):
  anc_ind_in_img = (activated_anc_ind >= idx * anc_per_img) & (activated_anc_ind < (idx+1) * anc_per_img)
  print('{} activated anchors!'.format(torch.sum(anc_ind_in_img)))
  data_visualizer(img, idx_to_class, bbox[:, :4], coord_trans(activated_anc_coord[anc_ind_in_img], w_list[idx], h_list[idx]))

print('*'*80)
print('Negative anchors:')
for img, bbox, idx in zip(img_list, box_list, torch.arange(box_list.shape[0])):
  anc_ind_in_img = (negative_anc_ind >= idx * anc_per_img) & (negative_anc_ind < (idx+1) * anc_per_img)
  print('{} negative anchors!'.format(torch.sum(anc_ind_in_img)))
  data_visualizer(img, idx_to_class, bbox[:, :4], coord_trans(negative_anc_coord[anc_ind_in_img], w_list[idx], h_list[idx]))

********************************************************************************
Activated (positive) anchors:
1 activated anchors!

2 activated anchors!

1 activated anchors!

********************************************************************************
Negative anchors:
1 negative anchors!

2 negative anchors!

1 negative anchors!

class PredictionNetwork(nn.Module):
    def __init__(self, in_dim, hidden_dim=128, num_anchors=9, num_classes=20, drop_ratio=0.3):
        super().__init__()

        assert(num_classes != 0 and num_anchors != 0)
        self.num_classes = num_classes
        self.num_anchors = num_anchors

        ##############################################################################
        # TODO: Set up a network that will predict outputs for all anchors. This     #
        # network should have a 1x1 convolution with hidden_dim filters, followed    #
        # by a Dropout layer with p=drop_ratio, a Leaky ReLU nonlinearity, and       #
        # finally another 1x1 convolution layer to predict all outputs. You can      #
        # use an nn.Sequential for this network, and store it in a member variable.  #
        # HINT: The output should be of shape (B, 5*A+C, 7, 7), where                #
        # A=self.num_anchors and C=self.num_classes.                                 #
        ##############################################################################

        self.out_dim = 5 * num_anchors + num_classes
        self.model = nn.Sequential(
          nn.Conv2d(in_channels=in_dim, out_channels=hidden_dim, kernel_size=1),
          nn.Dropout(p=drop_ratio),
          nn.LeakyReLU(),
          nn.Conv2d(in_channels=hidden_dim, out_channels=self.out_dim, kernel_size=1)
        )

        ##############################################################################
        #                               END OF YOUR CODE                             #
        ##############################################################################

    def _extract_anchor_data(self, anchor_data, anchor_idx):
        """
        Inputs:
        - anchor_data: Tensor of shape (B, A, D, H, W) giving a vector of length
          D for each of A anchors at each point in an H x W grid.
        - anchor_idx: int64 Tensor of shape (M,) giving anchor indices to extract

        Returns:
        - extracted_anchors: Tensor of shape (M, D) giving anchor data for each
          of the anchors specified by anchor_idx.
        """
        B, A, D, H, W = anchor_data.shape
        anchor_data = anchor_data.permute(0, 1, 3, 4, 2).contiguous().view(-1, D)
        extracted_anchors = anchor_data[anchor_idx]
        return extracted_anchors

    def _extract_class_scores(self, all_scores, anchor_idx):
        """
        Inputs:
        - all_scores: Tensor of shape (B, C, H, W) giving classification scores for
          C classes at each point in an H x W grid.
        - anchor_idx: int64 Tensor of shape (M,) giving the indices of anchors at
          which to extract classification scores

        Returns:
        - extracted_scores: Tensor of shape (M, C) giving the classification scores
          for each of the anchors specified by anchor_idx.
        """
        B, C, H, W = all_scores.shape
        A = self.num_anchors
        all_scores = all_scores.contiguous().permute(0, 2, 3, 1).contiguous()
        all_scores = all_scores.view(B, 1, H, W, C).expand(B, A, H, W, C)
        all_scores = all_scores.reshape(B * A * H * W, C)
        extracted_scores = all_scores[anchor_idx]
        return extracted_scores

    def forward(self, features, pos_anchor_idx=None, neg_anchor_idx=None):
        """
        Run the forward pass of the network to predict outputs given features
        from the backbone network.

        Inputs:
        - features: Tensor of shape (B, in_dim, 7, 7) giving image features computed
          by the backbone network.
        - pos_anchor_idx: int64 Tensor of shape (M,) giving the indices of anchors
          marked as positive. These are only given during training; at test-time
          this should be None.
        - neg_anchor_idx: int64 Tensor of shape (M,) giving the indices of anchors
          marked as negative. These are only given at training; at test-time this
          should be None.

        The outputs from this method are different during training and inference.

        During training, pos_anchor_idx and neg_anchor_idx are given and identify
        which anchors should be positive and negative, and this forward pass needs
        to extract only the predictions for the positive and negative anchors.

        During inference, only features are provided and this method needs to return
        predictions for all anchors.

        Outputs (During training):
        - conf_scores: Tensor of shape (2*M, 1) giving the predicted classification
          scores for positive anchors and negative anchors (in that order).
        - offsets: Tensor of shape (M, 4) giving predicted transformation for
          positive anchors.
        - class_scores: Tensor of shape (M, C) giving classification scores for
          positive anchors.

        Outputs (During inference):
        - conf_scores: Tensor of shape (B, A, H, W) giving predicted classification
          scores for all anchors.
        - offsets: Tensor of shape (B, A, 4, H, W) giving predicted transformations
          all all anchors.
        - class_scores: Tensor of shape (B, C, H, W) giving classification scores for
          each spatial position.
        """
        conf_scores, offsets, class_scores = None, None, None
        ############################################################################
        # TODO: Use backbone features to predict conf_scores, offsets, and         #
        # class_scores. Make sure conf_scores is between 0 and 1 by squashing the  #
        # network output with a sigmoid. Also make sure the first two elements t^x #
        # and t^y of offsets are between -0.5 and 0.5 by squashing with a sigmoid  #
        # and subtracting 0.5.                                                     #
        #                                                                          #
        # During training you need to extract the outputs for only the positive    #
        # and negative anchors as specified above.                                 #
        #                                                                          #
        # HINT: You can use the provided helper methods self._extract_anchor_data  #
        # and self._extract_class_scores to extract information for positive and   #
        # negative anchors specified by pos_anchor_idx and neg_anchor_idx.         #
        ############################################################################

        B, _, H, W = features.shape
        scores = self.model(features) # (B, 5*A+C, 7, 7)

        # Split scores into anchor scores and class scores
        anchor_scores = scores[:, :5*self.num_anchors, :, :]    # (B, 5*A, 7, 7)
        anchor_scores = anchor_scores.view(B, self.num_anchors, 5, 7, 7)    # (B, A, 5, 7, 7)
        class_scores = scores[:, 5*self.num_anchors:, :, :]     # (B, C, 7, 7)

        # Training: pos and neg anchors are not none
        if pos_anchor_idx is not None and neg_anchor_idx is not None:
            # Calculate positive and negative confidence scores separately then concatenate
            pos_anchor_data = self._extract_anchor_data(anchor_scores, pos_anchor_idx)    # (M, 5)
            neg_anchor_data = self._extract_anchor_data(anchor_scores, neg_anchor_idx)    # (M, 5)
            conf_scores = torch.cat((pos_anchor_data[:, 0:1], neg_anchor_data[:, 0:1]), dim=0)  # (2*M, 1)
            conf_scores = torch.sigmoid(conf_scores) # Squash with a sigmoid

            offsets = pos_anchor_data[:, 1:5]    # (M, 4)
            offsets[:, :2] = torch.sigmoid(offsets[:, :2]) - 0.5  # Normalize tx and ty

            # Format class data for _extract_class_scores
            class_scores = self._extract_class_scores(class_scores, pos_anchor_idx) # (M, C)

        # Testing: pos and neg anchors are none
        else:
            conf_scores = anchor_scores[:, :, 0, :, :]  # (B, A, 1, 7, 7)
            conf_scores = conf_scores.squeeze(2)        # (B, A, 7, 7)
            conf_scores = torch.sigmoid(conf_scores) # Squash with a sigmoid

            offsets = anchor_scores[:, :, 1:5, :, :]     # (B, A, 4, 7, 7)
            offsets[:, :, :2, :, :] = torch.sigmoid(offsets[:, :, :2, :, :]) - 0.5  # Normalize tx and ty

        ##############################################################################
        #                               END OF YOUR CODE                             #
        ##############################################################################
        return conf_scores, offsets, class_scores

# sanity check
fix_random_seed(0)
pred_network = PredictionNetwork(1280, drop_ratio=0).to(**to_float_cuda)
features = torch.linspace(-10., 10., steps=3*1280*7*7, **to_float_cuda).view(3, 1280, 7, 7)
pos_anchor_idx = torch.tensor([122, 605, 871, 955], **to_long_cuda)
neg_anchor_idx = torch.tensor([1048, 1292, 1124, 1092], **to_long_cuda)
conf_scores, offsets, class_prob = pred_network(features, pos_anchor_idx, neg_anchor_idx)

expected_conf_scores = torch.tensor([[0.85080749], [0.55230302], [0.45239496], [0.51819414],
                                     [0.38467780], [0.82627463], [0.53837817], [0.538446366]], **to_float_cuda)
expected_offsets = torch.tensor([[-0.16344640,  0.45598251, -1.56108809, -1.96177566],
                                 [ 0.09087485, -0.11321104,  0.46109992,  0.13557276],
                                 [ 0.14098871, -0.04003078, -0.14689390,  0.01480492],
                                 [-0.22611487,  0.14513946, -1.26124716,  0.58137202]], **to_float_cuda)
expected_class_prob = torch.tensor([0.88649291, 1.08752346, 0.90241265, 1.28021181, 0.77324629,
                                    -0.93609941, 1.44269419, -1.01072836, 0.09888625, -0.09179375,
                                    -0.48814785, -1.14749694, 0.24533349, -0.19522685, 0.21445289,
                                    0.91779679, 0.97305167, 1.01984429, -0.11932681, 0.34998628], **to_float_cuda)

error = rel_error(conf_scores, expected_conf_scores)
result = "Pass" if error < 1e-4 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Confidence Scores Relative Error: {error}\n{color}Result: {result}\033[0m')

error = rel_error(offsets, expected_offsets)
result = "Pass" if error < 1e-4 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Offsets Relative Error: {error}\n{color}Result: {result}\033[0m')

error = rel_error(class_prob.mean(0), expected_class_prob)
result = "Pass" if error < 1e-4 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Class Probabilities Relative Error: {error}\n{color}Result: {result}\033[0m')

Confidence Scores Relative Error: 2.3242040469995118e-07
Result: Pass
Offsets Relative Error: 2.4281827791128308e-05
Result: Pass
Class Probabilities Relative Error: 1.3798310192214558e-06
Result: Pass

def ConfScoreRegression(conf_scores, GT_conf_scores):
  """
  Use sum-squared error as in YOLO

  Inputs:
  - conf_scores: Predicted confidence scores
  - GT_conf_scores: GT confidence scores

  Outputs:
  - conf_score_loss
  """
  # the target conf_scores for negative samples are zeros
  GT_conf_scores = torch.cat((torch.ones_like(GT_conf_scores), \
                              torch.zeros_like(GT_conf_scores)), dim=0).view(-1, 1)
  conf_score_loss = torch.sum((conf_scores - GT_conf_scores)**2) * 1. / GT_conf_scores.shape[0]
  return conf_score_loss

def BboxRegression(offsets, GT_offsets):
  """"
  Use sum-squared error as in YOLO
  For both xy and wh

  Inputs:
  - offsets: Predicted box offsets
  - GT_offsets: GT box offsets

  Outputs:
  - bbox_reg_loss
  """
  bbox_reg_loss = torch.sum((offsets - GT_offsets)**2) * 1. / GT_offsets.shape[0]
  return bbox_reg_loss

def ObjectClassification(class_prob, GT_class, batch_size, anc_per_img, activated_anc_ind):
  """"
  Use softmax loss

  Inputs:
  - class_prob: Predicted softmax class probability
  - GT_class: GT box class label

  Outputs:
  - object_cls_loss
  """
  # average within sample and then average across batch
  # such that the class pred would not bias towards dense popular objects like `person`
  all_loss = F.cross_entropy(class_prob, GT_class, reduction='none') # , reduction='sum') * 1. / batch_size
  object_cls_loss = 0
  for idx in range(batch_size):
    anc_ind_in_img = (activated_anc_ind >= idx * anc_per_img) & (activated_anc_ind < (idx+1) * anc_per_img)
    object_cls_loss += all_loss[anc_ind_in_img].sum() * 1. / torch.sum(anc_ind_in_img)
  object_cls_loss /= batch_size
  # object_cls_loss = F.cross_entropy(class_prob, GT_class, reduction='sum') * 1. / batch_size

  return object_cls_loss

# sanity check
conf_loss = ConfScoreRegression(conf_scores, GT_conf_scores)
reg_loss = BboxRegression(offsets, GT_offsets)
cls_loss = ObjectClassification(class_prob, GT_class, w_list.shape[0], anc_per_img, activated_anc_ind)
print('conf loss: {:.4f}, reg loss: {:.4f}, cls loss: {:.4f}'.format(conf_loss, reg_loss, cls_loss))

loss_all = torch.tensor([conf_loss.data, reg_loss.data, cls_loss.data], **to_float_cuda)
expected_loss = torch.tensor([0.27064770460128784, 2.9750006198883057, 2.3919472694396973], **to_float_cuda)

error = rel_error(loss_all, expected_loss)
result = "Pass" if error < 1e-6 else "Fail"
color = "\033[92m" if result == "Pass" else "\033[91m"
print(f'Loss Relative Error: {error}\n{color}Result: {result}\033[0m')

conf loss: 0.2706, reg loss: 2.9750, cls loss: 2.3919
Loss Relative Error: 1.9935103523494035e-07
Result: Pass

class SingleStageDetector(nn.Module):
  def __init__(self):
    super().__init__()

    self.anchor_list = torch.tensor([[1, 1], [2, 2], [3, 3], [4, 4], [5, 5], [2, 3], [3, 2], [3, 5], [5, 3]]) # READ ONLY
    self.feat_extractor = FeatureExtractor()
    self.num_classes = 20
    self.pred_network = PredictionNetwork(1280, num_anchors=self.anchor_list.shape[0], \
                                          num_classes=self.num_classes)
  def forward(self):
    raise NotImplementedError

  def inference(self):
    raise NotImplementedError

  def detector_forward(self, images, bboxes):
    """
    Training-time forward pass for the single-stage detector.

    Inputs:
    - images: Input images, of shape (B, 3, 224, 224)
    - bboxes: GT bounding boxes of shape (B, N, 5) (padded)

    Outputs:
    - total_loss: Torch scalar giving the total loss for the batch.
    """
    # weights to multiple to each loss term
    w_conf = 1 # for conf_scores
    w_reg = 1 # for offsets
    w_cls = 1 # for class_prob

    total_loss = None
    ##############################################################################
    # TODO: Implement the forward pass of SingleStageDetector.                   #
    # A few key steps are outlined as follows:                                   #
    # i) Image feature extraction,                                               #
    # ii) Grid and anchor generation,                                            #
    # iii) Compute IoU between anchors and GT boxes and then determine activated/#
    #      negative anchors, and GT_conf_scores, GT_offsets, GT_class,           #
    # iv) Compute conf_scores, offsets, class_prob through the prediction network#
    # v) Compute the total_loss which is formulated as:                          #
    #    total_loss = w_conf * conf_loss + w_reg * reg_loss + w_cls * cls_loss,  #
    #    where conf_loss is determined by ConfScoreRegression, w_reg by          #
    #    BboxRegression, and w_cls by ObjectClassification.                      #
    # HINT: Set `neg_thresh=0.2` in ReferenceOnActivatedAnchors in this notebook #
    #       (A5-1) for a better performance than with the default value.         #
    ##############################################################################

    # Step i) Image feature extraction
    feat = self.feat_extractor(images)

    # Step ii) Grid and anchor generation
    B, _, H, W = feat.shape
    grid = GenerateGrid(batch_size=B, w_amap=H, h_amap=W)
    anchors = GenerateAnchor(anc=self.anchor_list.to('cuda'), grid=grid)

    # Step iii) Compute IoU between anchors and GT boxes and then determine activated/negative anchors, and GT_conf_scores, GT_offsets, GT_class
    # Note: Treat anchors as region proposals for setting up the training set
    iou_mat = IoU(proposals=anchors, bboxes=bboxes)
    activated_anc_ind, negative_anc_ind, GT_conf_scores, GT_offsets, GT_class, _, _ = ReferenceOnActivatedAnchors(anchors=anchors, bboxes=bboxes, grid=grid, iou_mat=iou_mat)

    # Step iv) Compute conf_scores, offsets, class_prob through the prediction network
    conf_scores, offsets, class_scores = self.pred_network.forward(features=feat, pos_anchor_idx=activated_anc_ind, neg_anchor_idx=negative_anc_ind)

    # Step v) Compute the total_loss
    conf_score_loss = ConfScoreRegression(conf_scores, GT_conf_scores)
    bbox_reg_loss = BboxRegression(offsets, GT_offsets)

    # Total anchors per image = A * H' * W'
    anc_per_img = anchors.shape[1] * anchors.shape[2] * anchors.shape[3]
    object_cls_loss = ObjectClassification(class_scores, GT_class, B, anc_per_img, activated_anc_ind)

    total_loss = w_conf * conf_score_loss + w_reg * bbox_reg_loss + w_cls * object_cls_loss

    ##############################################################################
    #                               END OF YOUR CODE                             #
    ##############################################################################

    return total_loss

  SingleStageDetector.forward = detector_forward

def DetectionSolver(detector, train_loader, learning_rate=3e-3,
                    lr_decay=1, num_epochs=20, **kwargs):
  """
  Run optimization to train the model.
  """

  # ship model to GPU
  detector.to(**to_float_cuda)

  # optimizer setup
  from torch import optim
  # optimizer = optim.Adam(
  optimizer = optim.SGD(
    filter(lambda p: p.requires_grad, detector.parameters()),
    learning_rate) # leave betas and eps by default
  lr_scheduler = optim.lr_scheduler.LambdaLR(optimizer,
                                             lambda epoch: lr_decay ** epoch)

  # sample minibatch data
  loss_history = []
  detector.train()
  for i in range(num_epochs):
    start_t = time.time()
    for iter_num, data_batch in enumerate(train_loader):
      images, boxes, w_batch, h_batch, _ = data_batch
      resized_boxes = coord_trans(boxes, w_batch, h_batch, mode='p2a')
      images = images.to(**to_float_cuda)
      resized_boxes = resized_boxes.to(**to_float_cuda)

      loss = detector(images, resized_boxes)
      optimizer.zero_grad()
      loss.backward()
      loss_history.append(loss.item())
      optimizer.step()

      print('(Iter {} / {})'.format(iter_num, len(train_loader)))

    end_t = time.time()
    print('(Epoch {} / {}) loss: {:.4f} time per epoch: {:.1f}s'.format(
        i, num_epochs, loss.item(), end_t-start_t))

    lr_scheduler.step()

  # plot the training losses
  plt.plot(loss_history)
  plt.xlabel('Iteration')
  plt.ylabel('Loss')
  plt.title('Training loss history')
  plt.show()

# monitor the training loss
fix_random_seed(0)
num_sample = 10
small_dataset = torch.utils.data.Subset(train_dataset, torch.linspace(0, len(train_dataset)-1, steps=num_sample).long())
small_train_loader = pascal_voc2007_loader(small_dataset, 10) # a new loader

for lr in [1e-2]:
  print('lr: ', lr)
  detector = SingleStageDetector()
  DetectionSolver(detector, small_train_loader, learning_rate=lr, num_epochs=200)

lr:  0.01
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 0 / 200) loss: 4.7055 time per epoch: 0.7s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 1 / 200) loss: 3.3176 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 2 / 200) loss: 2.7884 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 3 / 200) loss: 2.3024 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 4 / 200) loss: 1.9575 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 5 / 200) loss: 1.8149 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 6 / 200) loss: 1.6512 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 7 / 200) loss: 1.5679 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 8 / 200) loss: 1.5888 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 9 / 200) loss: 1.4474 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 10 / 200) loss: 1.2730 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 11 / 200) loss: 1.3825 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 12 / 200) loss: 1.2727 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 13 / 200) loss: 1.2793 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 14 / 200) loss: 1.3265 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 15 / 200) loss: 1.0746 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 16 / 200) loss: 1.0831 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 17 / 200) loss: 1.0438 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 18 / 200) loss: 1.0242 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 19 / 200) loss: 1.0730 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 20 / 200) loss: 0.8876 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 21 / 200) loss: 0.8964 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 22 / 200) loss: 0.8490 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 23 / 200) loss: 0.8339 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 24 / 200) loss: 1.0244 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 25 / 200) loss: 0.9089 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 26 / 200) loss: 0.8901 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 27 / 200) loss: 0.8397 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 28 / 200) loss: 0.9323 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 29 / 200) loss: 0.8734 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 30 / 200) loss: 0.7738 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 31 / 200) loss: 0.7443 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 32 / 200) loss: 0.7393 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 33 / 200) loss: 0.7780 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 34 / 200) loss: 0.7163 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 35 / 200) loss: 0.6919 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 36 / 200) loss: 0.7152 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 37 / 200) loss: 0.6678 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 38 / 200) loss: 0.6863 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 39 / 200) loss: 0.7363 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 40 / 200) loss: 0.6296 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 41 / 200) loss: 0.6489 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 42 / 200) loss: 0.6170 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 43 / 200) loss: 0.6557 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 44 / 200) loss: 0.5875 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 45 / 200) loss: 0.6708 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 46 / 200) loss: 0.6137 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 47 / 200) loss: 0.5934 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 48 / 200) loss: 0.5678 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 49 / 200) loss: 0.6098 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 50 / 200) loss: 0.6634 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 51 / 200) loss: 0.5339 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 52 / 200) loss: 0.5502 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 53 / 200) loss: 0.5678 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 54 / 200) loss: 0.5735 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 55 / 200) loss: 0.5802 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 56 / 200) loss: 0.5291 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 57 / 200) loss: 0.5338 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 58 / 200) loss: 0.6161 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 59 / 200) loss: 0.5330 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 60 / 200) loss: 0.5765 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 61 / 200) loss: 0.4776 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 62 / 200) loss: 0.4829 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 63 / 200) loss: 0.5373 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 64 / 200) loss: 0.4594 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 65 / 200) loss: 0.4720 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 66 / 200) loss: 0.5014 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 67 / 200) loss: 0.5064 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 68 / 200) loss: 0.4712 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 69 / 200) loss: 0.5442 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 70 / 200) loss: 0.4994 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 71 / 200) loss: 0.5291 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 72 / 200) loss: 0.4848 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 73 / 200) loss: 0.4090 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 74 / 200) loss: 0.5120 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 75 / 200) loss: 0.3948 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 76 / 200) loss: 0.4039 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 77 / 200) loss: 0.4117 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 78 / 200) loss: 0.4072 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 79 / 200) loss: 0.4661 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 80 / 200) loss: 0.3804 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 81 / 200) loss: 0.4173 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 82 / 200) loss: 0.4545 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 83 / 200) loss: 0.4311 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 84 / 200) loss: 0.4097 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 85 / 200) loss: 0.3794 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 86 / 200) loss: 0.4122 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 87 / 200) loss: 0.3680 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 88 / 200) loss: 0.4250 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 89 / 200) loss: 0.3600 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 90 / 200) loss: 0.4715 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 91 / 200) loss: 0.4343 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 92 / 200) loss: 0.4237 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 93 / 200) loss: 0.3751 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 94 / 200) loss: 0.4167 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 95 / 200) loss: 0.4014 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 96 / 200) loss: 0.3809 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 97 / 200) loss: 0.3886 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 98 / 200) loss: 0.4597 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 99 / 200) loss: 0.4131 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 100 / 200) loss: 0.4168 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 101 / 200) loss: 0.4119 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 102 / 200) loss: 0.4208 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 103 / 200) loss: 0.4212 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 104 / 200) loss: 0.3861 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 105 / 200) loss: 0.3687 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 106 / 200) loss: 0.3939 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 107 / 200) loss: 0.3740 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 108 / 200) loss: 0.3567 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 109 / 200) loss: 0.3658 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 110 / 200) loss: 0.3384 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 111 / 200) loss: 0.3925 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 112 / 200) loss: 0.3623 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 113 / 200) loss: 0.3816 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 114 / 200) loss: 0.3962 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 115 / 200) loss: 0.3334 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 116 / 200) loss: 0.3320 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 117 / 200) loss: 0.3400 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 118 / 200) loss: 0.3754 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 119 / 200) loss: 0.3450 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 120 / 200) loss: 0.3350 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 121 / 200) loss: 0.3191 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 122 / 200) loss: 0.3954 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 123 / 200) loss: 0.3812 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 124 / 200) loss: 0.3851 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 125 / 200) loss: 0.3645 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 126 / 200) loss: 0.3208 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 127 / 200) loss: 0.3754 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 128 / 200) loss: 0.3375 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 129 / 200) loss: 0.3686 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 130 / 200) loss: 0.3078 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 131 / 200) loss: 0.3173 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 132 / 200) loss: 0.2828 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 133 / 200) loss: 0.3723 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 134 / 200) loss: 0.3239 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 135 / 200) loss: 0.3220 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 136 / 200) loss: 0.2958 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 137 / 200) loss: 0.3092 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 138 / 200) loss: 0.3747 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 139 / 200) loss: 0.3488 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 140 / 200) loss: 0.3066 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 141 / 200) loss: 0.2979 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 142 / 200) loss: 0.3464 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 143 / 200) loss: 0.2944 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 144 / 200) loss: 0.2905 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 145 / 200) loss: 0.3088 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 146 / 200) loss: 0.2765 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 147 / 200) loss: 0.3044 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 148 / 200) loss: 0.2738 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 149 / 200) loss: 0.2975 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 150 / 200) loss: 0.3449 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 151 / 200) loss: 0.3389 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 152 / 200) loss: 0.3263 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 153 / 200) loss: 0.3293 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 154 / 200) loss: 0.3067 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 155 / 200) loss: 0.2433 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 156 / 200) loss: 0.2765 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 157 / 200) loss: 0.2699 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 158 / 200) loss: 0.2923 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 159 / 200) loss: 0.2670 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 160 / 200) loss: 0.3146 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 161 / 200) loss: 0.2842 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 162 / 200) loss: 0.2654 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 163 / 200) loss: 0.2972 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 164 / 200) loss: 0.2837 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 165 / 200) loss: 0.2673 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 166 / 200) loss: 0.2538 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 167 / 200) loss: 0.2799 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 168 / 200) loss: 0.2699 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 169 / 200) loss: 0.2911 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 170 / 200) loss: 0.2922 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 171 / 200) loss: 0.2584 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 172 / 200) loss: 0.2341 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 173 / 200) loss: 0.2772 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 174 / 200) loss: 0.2817 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 175 / 200) loss: 0.2685 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 176 / 200) loss: 0.2643 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 177 / 200) loss: 0.2794 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 178 / 200) loss: 0.2729 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 179 / 200) loss: 0.2525 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 180 / 200) loss: 0.2460 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 181 / 200) loss: 0.2761 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 182 / 200) loss: 0.2833 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 183 / 200) loss: 0.2706 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 184 / 200) loss: 0.2584 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 185 / 200) loss: 0.2362 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 186 / 200) loss: 0.2874 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 187 / 200) loss: 0.2682 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 188 / 200) loss: 0.2685 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 189 / 200) loss: 0.2504 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 190 / 200) loss: 0.2500 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 191 / 200) loss: 0.2791 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 192 / 200) loss: 0.2520 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 193 / 200) loss: 0.2915 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 194 / 200) loss: 0.2309 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 195 / 200) loss: 0.2583 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 196 / 200) loss: 0.2349 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 197 / 200) loss: 0.2555 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 198 / 200) loss: 0.2857 time per epoch: 0.1s
number of pos proposals:  50
(Iter 0 / 1)
(Epoch 199 / 200) loss: 0.2381 time per epoch: 0.1s

# monitor the training loss
train_loader = pascal_voc2007_loader(train_dataset, 100) # a new loader

lr = 5e-2
num_epochs = 50
yolo_detector = SingleStageDetector()
DetectionSolver(yolo_detector, train_loader, learning_rate=lr, num_epochs=num_epochs)

number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 0 / 50) loss: 2.2848 time per epoch: 23.7s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 1 / 50) loss: 1.6205 time per epoch: 23.4s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 2 / 50) loss: 1.2404 time per epoch: 24.4s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 3 / 50) loss: 1.0612 time per epoch: 24.1s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 4 / 50) loss: 0.8194 time per epoch: 23.6s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 5 / 50) loss: 0.7869 time per epoch: 23.7s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 6 / 50) loss: 0.7006 time per epoch: 23.9s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 7 / 50) loss: 0.7018 time per epoch: 25.0s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 8 / 50) loss: 0.5543 time per epoch: 24.3s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
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(Epoch 9 / 50) loss: 0.5209 time per epoch: 23.9s
number of pos proposals:  348
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(Epoch 10 / 50) loss: 0.5040 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 11 / 50) loss: 0.4371 time per epoch: 23.8s
number of pos proposals:  348
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(Epoch 12 / 50) loss: 0.4198 time per epoch: 23.9s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 13 / 50) loss: 0.4264 time per epoch: 24.3s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 14 / 50) loss: 0.3714 time per epoch: 23.7s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 15 / 50) loss: 0.3733 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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number of pos proposals:  375
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(Epoch 16 / 50) loss: 0.3569 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 17 / 50) loss: 0.3523 time per epoch: 23.7s
number of pos proposals:  348
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number of pos proposals:  410
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number of pos proposals:  375
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(Epoch 18 / 50) loss: 0.3281 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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number of pos proposals:  375
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(Epoch 19 / 50) loss: 0.3207 time per epoch: 24.1s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 20 / 50) loss: 0.2923 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 21 / 50) loss: 0.3155 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 22 / 50) loss: 0.2986 time per epoch: 23.8s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 23 / 50) loss: 0.2658 time per epoch: 23.7s
number of pos proposals:  348
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number of pos proposals:  410
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(Epoch 24 / 50) loss: 0.2700 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 25 / 50) loss: 0.2706 time per epoch: 24.2s
number of pos proposals:  348
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(Epoch 26 / 50) loss: 0.2818 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 27 / 50) loss: 0.2395 time per epoch: 23.7s
number of pos proposals:  348
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(Epoch 28 / 50) loss: 0.2299 time per epoch: 23.7s
number of pos proposals:  348
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(Epoch 29 / 50) loss: 0.2487 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 30 / 50) loss: 0.2412 time per epoch: 23.7s
number of pos proposals:  348
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(Epoch 31 / 50) loss: 0.2322 time per epoch: 24.3s
number of pos proposals:  348
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(Epoch 32 / 50) loss: 0.2120 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 33 / 50) loss: 0.2250 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 34 / 50) loss: 0.1981 time per epoch: 23.5s
number of pos proposals:  348
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(Epoch 35 / 50) loss: 0.2044 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 36 / 50) loss: 0.2041 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 37 / 50) loss: 0.1874 time per epoch: 24.2s
number of pos proposals:  348
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(Epoch 38 / 50) loss: 0.2101 time per epoch: 23.7s
number of pos proposals:  348
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(Epoch 39 / 50) loss: 0.1956 time per epoch: 23.6s
number of pos proposals:  348
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(Epoch 40 / 50) loss: 0.2072 time per epoch: 23.7s
number of pos proposals:  348
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(Epoch 41 / 50) loss: 0.1954 time per epoch: 23.7s
number of pos proposals:  348
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number of pos proposals:  348
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(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 45 / 50) loss: 0.1829 time per epoch: 23.6s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 46 / 50) loss: 0.1743 time per epoch: 23.6s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 47 / 50) loss: 0.1730 time per epoch: 23.6s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 48 / 50) loss: 0.1677 time per epoch: 24.4s
number of pos proposals:  348
(Iter 0 / 25)
number of pos proposals:  410
(Iter 1 / 25)
number of pos proposals:  375
(Iter 2 / 25)
number of pos proposals:  362
(Iter 3 / 25)
number of pos proposals:  348
(Iter 4 / 25)
number of pos proposals:  316
(Iter 5 / 25)
number of pos proposals:  379
(Iter 6 / 25)
number of pos proposals:  362
(Iter 7 / 25)
number of pos proposals:  368
(Iter 8 / 25)
number of pos proposals:  363
(Iter 9 / 25)
number of pos proposals:  325
(Iter 10 / 25)
number of pos proposals:  355
(Iter 11 / 25)
number of pos proposals:  346
(Iter 12 / 25)
number of pos proposals:  363
(Iter 13 / 25)
number of pos proposals:  357
(Iter 14 / 25)
number of pos proposals:  349
(Iter 15 / 25)
number of pos proposals:  370
(Iter 16 / 25)
number of pos proposals:  367
(Iter 17 / 25)
number of pos proposals:  395
(Iter 18 / 25)
number of pos proposals:  386
(Iter 19 / 25)
number of pos proposals:  392
(Iter 20 / 25)
number of pos proposals:  405
(Iter 21 / 25)
number of pos proposals:  335
(Iter 22 / 25)
number of pos proposals:  359
(Iter 23 / 25)
number of pos proposals:  376
(Iter 24 / 25)
(Epoch 49 / 50) loss: 0.1809 time per epoch: 23.7s

# (optional) load/save checkpoint
# torch.save(yolo_detector.state_dict(), 'yolo_detector.pt') # uncomment to save your checkpoint

# yolo_detector = SingleStageDetector()
# yolo_detector.load_state_dict(torch.load('yolo_detector.pt')) # uncomment to load your previous checkpoint

def nms(boxes, scores, iou_threshold=0.5, topk=None):
    """
    Non-maximum suppression removes overlapping bounding boxes.

    Inputs:
    - boxes: top-left and bottom-right coordinate values of the bounding boxes
      to perform NMS on, of shape Nx4
    - scores: scores for each one of the boxes, of shape N
    - iou_threshold: discards all overlapping boxes with IoU > iou_threshold; float
    - topk: If this is not None, then return only the topk highest-scoring boxes.
      Otherwise if this is None, then return all boxes that pass NMS.

    Outputs:
    - keep: torch.long tensor with the indices of the elements that have been
      kept by NMS, sorted in decreasing order of scores; of shape [num_kept_boxes]
    """

    if (not boxes.numel()) or (not scores.numel()):
        return torch.zeros(0, dtype=torch.long)

    keep = None
    #############################################################################
    # TODO: Implement non-maximum suppression which iterates the following:     #
    #       1. Select the highest-scoring box among the remaining ones,         #
    #          which has not been chosen in this step before                    #
    #       2. Eliminate boxes with IoU > threshold                             #
    #       3. If any boxes remain, GOTO 1                                      #
    #       Your implementation should not depend on a specific device type;    #
    #       you can use the device of the input if necessary.                   #
    # HINT: You can refer to the torchvision library code:                      #
    #   github.com/pytorch/vision/blob/master/torchvision/csrc/cpu/nms_cpu.cpp  #
    #############################################################################

    # Helper function for finding the IoU between the highest-scoring box and all others
    def iou_helper(reference, candidates):
        """
        Compute the IoU between two set of bounding boxes

        Inputs:
        - reference: A single bounding box with dimension (1, 4)
        - candidates: Multiple bounding boxes with dimension (A, 4)

        Output:
        - ious: Tensor of IoUs between the reference and candidates with dimension (A, 1)
        """

        # Find the inner corners of the intersection
        min_bottom_right = torch.min(reference[:, 2:], candidates[:, 2:])
        max_top_left = torch.max(reference[:, :2], candidates[:, :2])

        # Remove negative intersections
        intersection = torch.clamp(min_bottom_right - max_top_left, min=0)
        intersection_area = intersection[:, 0] * intersection[:, 1]

        # Find the individual areas
        reference_area = ((reference[:, 2]-reference[:, 0]) * (reference[:, 3]-reference[:, 1]))
        candidates_area = ((candidates[:, 2]-candidates[:, 0]) * (candidates[:, 3]-candidates[:, 1]))

        # Calculate the union area by adding a dimension to reference_area so it can be broadcast
        union_area = reference_area.unsqueeze(1) + candidates_area - intersection_area

        return intersection_area / union_area

    # Sort all bounding boxes by descending score
    scores, idx = scores.sort(descending=True)
    boxes = boxes[idx]

    # Iterate through all boxes until they're all kept or eliminated
    keep = []
    while boxes.size(0):
        # Process the remaining box with the highest score
        current = boxes[0].unsqueeze(0)

        # Store the index of the box with highest score in keep
        keep.append(idx[0].item())

        # Stop if current is the last box (no need to eliminate the others)
        if boxes.size(0) == 1:
            break

        # Calculate the IoU of the current box compared to all other boxes
        ious = iou_helper(current, boxes[1:])

        # Keep boxes with IoU less than the overlap threshold
        remaining = (ious < iou_threshold).squeeze()
        boxes = boxes[1:][remaining]
        idx = idx[1:][remaining]

        # If the top K results have been found
        if topk is not None and len(keep) >= topk:
            break

    keep = torch.tensor(keep, dtype=torch.long)

    #############################################################################
    #                              END OF YOUR CODE                             #
    #############################################################################
    return keep

fix_random_seed(0)
boxes = (100. * torch.rand(5000, 4)).round()
boxes[:,2] = boxes[:,2] + boxes[:,0] + 1.
boxes[:,3] = boxes[:,3] + boxes[:,1] + 1.
scores = torch.randn(5000)

names = ['your_cpu', 'torchvision_cpu', 'torchvision_cuda']
iou_thresholds = [0.3, 0.5, 0.7]
elapsed = dict(zip(names, [0.]*len(names)))
intersects = dict(zip(names[1:], [0.]*(len(names)-1)))

for iou_threshold in iou_thresholds:
  tic = time.time()
  my_keep = nms(boxes, scores, iou_threshold)
  elapsed['your_cpu'] += time.time() - tic

  tic = time.time()
  tv_keep = torchvision.ops.nms(boxes, scores, iou_threshold)
  elapsed['torchvision_cpu'] += time.time() - tic
  intersect = len(set(tv_keep.tolist()).intersection(my_keep.tolist()))
  intersects['torchvision_cpu'] += intersect / (len(my_keep) + len(tv_keep) - intersect)

  tic = time.time()
  tv_cuda_keep = torchvision.ops.nms(boxes.cuda(), scores.cuda(), iou_threshold).to(my_keep.device)
  torch.cuda.synchronize()
  elapsed['torchvision_cuda'] += time.time() - tic
  intersect = len(set(tv_cuda_keep.tolist()).intersection(my_keep.tolist()))
  intersects['torchvision_cuda'] += intersect / (len(my_keep) + len(tv_cuda_keep) - intersect)

for key in intersects:
  intersects[key] /= len(iou_thresholds)

# You should see < 1% difference
print('Testing NMS:')
print('Your        CPU  implementation: %fs' % elapsed['your_cpu'])
print('torchvision CPU  implementation: %fs' % elapsed['torchvision_cpu'])
print('torchvision CUDA implementation: %fs' % elapsed['torchvision_cuda'])
print('Speedup CPU : %fx' % (elapsed['your_cpu'] / elapsed['torchvision_cpu']))
print('Speedup CUDA: %fx' % (elapsed['your_cpu'] / elapsed['torchvision_cuda']))
print('Difference CPU : ', 1. - intersects['torchvision_cpu']) # in the order of 1e-3 or less
print('Difference CUDA: ', 1. - intersects['torchvision_cuda']) # in the order of 1e-3 or less

Testing NMS:
Your        CPU  implementation: 1.308229s
torchvision CPU  implementation: 0.073471s
torchvision CUDA implementation: 0.046161s
Speedup CPU : 17.806107x
Speedup CUDA: 28.340471x
Difference CPU :  0.002000423413535235
Difference CUDA:  0.005151022027271801

def detector_inference(self, images, thresh=0.5, nms_thresh=0.7):
    """"
    Inference-time forward pass for the single stage detector.

    Inputs:
    - images: Input images
    - thresh: Threshold value on confidence scores
    - nms_thresh: Threshold value on NMS

    Outputs:
    - final_propsals: Keeped proposals after confidence score thresholding and NMS,
                            a list of B (*x4) tensors
    - final_conf_scores: Corresponding confidence scores, a list of B (*x1) tensors
    - final_class: Corresponding class predictions, a list of B  (*x1) tensors
    """
    final_proposals, final_conf_scores, final_class = [], [], []
    ##############################################################################
    # TODO: Predicting the final proposal coordinates `final_proposals`,         #
    # confidence scores `final_conf_scores`, and the class index `final_class`.  #
    # The overall steps are similar to the forward pass but now you do not need  #
    # to decide the activated nor negative anchors.                              #
    # HINT: Thresholding the conf_scores based on the threshold value `thresh`.  #
    # Then, apply NMS (torchvision.ops.nms) to the filtered proposals given the  #
    # threshold `nms_thresh`.                                                    #
    # The class index is determined by the class with the maximal probability.   #
    # Note that `final_propsals`, `final_conf_scores`, and `final_class` are all #
    # lists of B 2-D tensors (you may need to unsqueeze dim=1 for the last two). #
    ##############################################################################

    with torch.no_grad():
        # Image feature extraction (same as train)
        features = self.feat_extractor(images)
        B, _, H, W = features.shape     # 10, _, 7, 7

        # Grid and anchor generation (same as train)
        grid = GenerateGrid(batch_size=B, w_amap=H, h_amap=W)
        anchors = GenerateAnchor(anc=self.anchor_list.to('cuda'), grid=grid)
        A = anchors.shape[1]    # 9

        # Compute conf_scores, offsets, class_prob through the prediction network
        conf_scores, offsets, class_probs = self.pred_network(features)
        C = class_probs.shape[1]    # 20

        # Skip deciding the activated and negative anchors

        # Generate proposals using the predicted offsets
        offsets = offsets.permute(0, 1, 3, 4, 2)  # (B, A, 4, H, W) --> (B, A, H, W, 4)
        proposals = GenerateProposal(anchors, offsets, method='YOLO')   # (B, A, H, W, 4)

        # Threshold the confidence scores then apply NMS
        # Note: Do this step one image at a time because the number of bounding boxes per image can change a lot
        for b in range(B):
            # conf_scores[b] --> ( 9, 7, 7)
            # proposals[b]   --> ( 9, 7, 7, 4)
            # class_probs[b] --> (20, 7, 7)

            # Flatten the scores, proposals, and class probabilities for easier comparison
            flat_conf = conf_scores[b].reshape(-1)              # (A*H*W) = (441)
            flat_proposals = proposals[b].reshape(-1, 4)        # (A*H*W, 4) = (441, 4)

            flat_class_probs = class_probs[b].permute(1, 2, 0)  # (H, W, C) = (7, 7, 20)
            flat_class_probs = flat_class_probs.unsqueeze(0).repeat(A, 1, 1, 1) # (A, H, W, C) = (9, 7, 7, 20)
            flat_class_probs = flat_class_probs.reshape(-1, C)  # (A*H*W, C) = (441, 20)

            # Filter out low confidence scores
            conf_mask = flat_conf > thresh
            filtered_conf = flat_conf[conf_mask]                # (441)
            filtered_proposals = flat_proposals[conf_mask]      # (441, 4)
            filtered_class_probs = flat_class_probs[conf_mask]  # (441, 20)

            # Apply NMS
            keep = nms(boxes=filtered_proposals, scores=filtered_conf, iou_threshold=nms_thresh)
            final_conf_scores.append(filtered_conf[keep].unsqueeze(-1).detach().cpu())  # (keep, 1)
            final_proposals.append(filtered_proposals[keep].detach().cpu())             # (keep, 4)
            final_class.append(torch.argmax(filtered_class_probs[keep], dim=1).unsqueeze(-1).detach().cpu())    # (keep, 1)

    ##############################################################################
    #                               END OF YOUR CODE                             #
    ##############################################################################
    return final_proposals, final_conf_scores, final_class

SingleStageDetector.inference = detector_inference

def DetectionInference(detector, data_loader, dataset, idx_to_class, thresh=0.8, nms_thresh=0.3, output_dir=None):

  # ship model to GPU
  detector.to(**to_float_cuda)

  detector.eval()
  start_t = time.time()

  if output_dir is not None:
    det_dir = 'mAP/input/detection-results'
    gt_dir = 'mAP/input/ground-truth'
    if os.path.exists(det_dir):
      shutil.rmtree(det_dir)
    os.mkdir(det_dir)
    if os.path.exists(gt_dir):
      shutil.rmtree(gt_dir)
    os.mkdir(gt_dir)

  for iter_num, data_batch in enumerate(data_loader):
    images, boxes, w_batch, h_batch, img_ids = data_batch
    images = images.to(**to_float_cuda)

    final_proposals, final_conf_scores, final_class = detector.inference(images, thresh=thresh, nms_thresh=nms_thresh)

    # clamp on the proposal coordinates
    batch_size = len(images)
    for idx in range(batch_size):
      torch.clamp_(final_proposals[idx][:, 0::2], min=0, max=w_batch[idx])
      torch.clamp_(final_proposals[idx][:, 1::2], min=0, max=h_batch[idx])

      # visualization
      # get the original image
      # hack to get the original image so we don't have to load from local again...
      i = batch_size*iter_num + idx
      img, _ = dataset.__getitem__(i)

      valid_box = sum([1 if j != -1 else 0 for j in boxes[idx][:, 0]])
      final_all = torch.cat((final_proposals[idx], \
        final_class[idx].float(), final_conf_scores[idx]), dim=-1).cpu()
      resized_proposals = coord_trans(final_all, w_batch[idx], h_batch[idx])

      # write results to file for evaluation (use mAP API https://github.com/Cartucho/mAP for now...)
      if output_dir is not None:
        file_name = img_ids[idx].replace('.jpg', '.txt')
        with open(os.path.join(det_dir, file_name), 'w') as f_det, \
          open(os.path.join(gt_dir, file_name), 'w') as f_gt:
          print('{}: {} GT bboxes and {} proposals'.format(img_ids[idx], valid_box, resized_proposals.shape[0]))
          for b in boxes[idx][:valid_box]:
            f_gt.write('{} {:.2f} {:.2f} {:.2f} {:.2f}\n'.format(idx_to_class[b[4].item()], b[0], b[1], b[2], b[3]))
          for b in resized_proposals:
            f_det.write('{} {:.6f} {:.2f} {:.2f} {:.2f} {:.2f}\n'.format(idx_to_class[b[4].item()], b[5], b[0], b[1], b[2], b[3]))
      else:
        data_visualizer(img, idx_to_class, boxes[idx][:valid_box], resized_proposals)

  end_t = time.time()
  print('Total inference time: {:.1f}s'.format(end_t-start_t))

# visualize the output from the overfitted model on small dataset
# the bounding boxes should be really accurate
DetectionInference(detector, small_train_loader, small_dataset, idx_to_class, thresh=0.8)

Total inference time: 2.3s

# visualize the same output from the model trained on the entire training set
# some bounding boxes might not make sense
DetectionInference(yolo_detector, small_train_loader, small_dataset, idx_to_class)

Total inference time: 2.9s

# Solve bug from mAP repo
# !pip install matplotlib==3.5.3

DetectionInference(yolo_detector, val_loader, val_dataset, idx_to_class, output_dir='mAP/input', thresh=0.8, nms_thresh=0.3)
# DetectionInference(yolo_detector, train_loader, train_dataset, idx_to_class, output_dir='mAP/input', thresh=0.8, nms_thresh=0.3) # uncomment to see training mAP
!cd mAP && python main.py

000005.jpg: 5 GT bboxes and 30 proposals
000007.jpg: 1 GT bboxes and 1 proposals
000009.jpg: 4 GT bboxes and 7 proposals
000016.jpg: 1 GT bboxes and 1 proposals
000019.jpg: 2 GT bboxes and 1 proposals
000020.jpg: 1 GT bboxes and 1 proposals
000021.jpg: 4 GT bboxes and 6 proposals
000024.jpg: 1 GT bboxes and 3 proposals
000030.jpg: 3 GT bboxes and 6 proposals
000039.jpg: 1 GT bboxes and 5 proposals
000041.jpg: 3 GT bboxes and 10 proposals
000046.jpg: 1 GT bboxes and 3 proposals
000050.jpg: 10 GT bboxes and 21 proposals
000051.jpg: 3 GT bboxes and 1 proposals
000052.jpg: 6 GT bboxes and 0 proposals
000060.jpg: 5 GT bboxes and 3 proposals
000063.jpg: 2 GT bboxes and 1 proposals
000065.jpg: 1 GT bboxes and 2 proposals
000072.jpg: 2 GT bboxes and 7 proposals
000081.jpg: 9 GT bboxes and 20 proposals
000093.jpg: 2 GT bboxes and 1 proposals
000095.jpg: 2 GT bboxes and 1 proposals
000099.jpg: 1 GT bboxes and 3 proposals
000101.jpg: 2 GT bboxes and 2 proposals
000102.jpg: 2 GT bboxes and 6 proposals
000107.jpg: 3 GT bboxes and 17 proposals
000109.jpg: 1 GT bboxes and 2 proposals
000110.jpg: 3 GT bboxes and 1 proposals
000113.jpg: 1 GT bboxes and 5 proposals
000117.jpg: 1 GT bboxes and 3 proposals
000118.jpg: 1 GT bboxes and 3 proposals
000120.jpg: 1 GT bboxes and 1 proposals
000121.jpg: 2 GT bboxes and 1 proposals
000123.jpg: 1 GT bboxes and 1 proposals
000125.jpg: 4 GT bboxes and 10 proposals
000130.jpg: 1 GT bboxes and 17 proposals
000131.jpg: 3 GT bboxes and 9 proposals
000132.jpg: 1 GT bboxes and 2 proposals
000142.jpg: 1 GT bboxes and 3 proposals
000143.jpg: 1 GT bboxes and 4 proposals
000146.jpg: 1 GT bboxes and 3 proposals
000150.jpg: 5 GT bboxes and 4 proposals
000156.jpg: 3 GT bboxes and 3 proposals
000158.jpg: 1 GT bboxes and 1 proposals
000165.jpg: 1 GT bboxes and 3 proposals
000169.jpg: 6 GT bboxes and 6 proposals
000170.jpg: 6 GT bboxes and 7 proposals
000177.jpg: 9 GT bboxes and 27 proposals
000180.jpg: 1 GT bboxes and 3 proposals
000184.jpg: 4 GT bboxes and 11 proposals
000190.jpg: 13 GT bboxes and 13 proposals
000203.jpg: 1 GT bboxes and 8 proposals
000208.jpg: 6 GT bboxes and 16 proposals
000210.jpg: 4 GT bboxes and 10 proposals
000211.jpg: 1 GT bboxes and 10 proposals
000214.jpg: 1 GT bboxes and 1 proposals
000215.jpg: 1 GT bboxes and 1 proposals
000218.jpg: 3 GT bboxes and 5 proposals
000221.jpg: 1 GT bboxes and 1 proposals
000224.jpg: 2 GT bboxes and 12 proposals
000229.jpg: 11 GT bboxes and 31 proposals
000232.jpg: 5 GT bboxes and 5 proposals
000233.jpg: 2 GT bboxes and 3 proposals
000236.jpg: 1 GT bboxes and 3 proposals
000241.jpg: 3 GT bboxes and 15 proposals
000244.jpg: 1 GT bboxes and 1 proposals
000245.jpg: 2 GT bboxes and 3 proposals
000246.jpg: 1 GT bboxes and 1 proposals
000249.jpg: 8 GT bboxes and 15 proposals
000251.jpg: 7 GT bboxes and 16 proposals
000257.jpg: 3 GT bboxes and 5 proposals
000266.jpg: 2 GT bboxes and 1 proposals
000268.jpg: 2 GT bboxes and 2 proposals
000269.jpg: 10 GT bboxes and 18 proposals
000270.jpg: 1 GT bboxes and 4 proposals
000275.jpg: 2 GT bboxes and 8 proposals
000285.jpg: 4 GT bboxes and 7 proposals
000289.jpg: 1 GT bboxes and 14 proposals
000298.jpg: 1 GT bboxes and 1 proposals
000302.jpg: 7 GT bboxes and 19 proposals
000303.jpg: 6 GT bboxes and 2 proposals
000304.jpg: 1 GT bboxes and 1 proposals
000305.jpg: 2 GT bboxes and 2 proposals
000308.jpg: 6 GT bboxes and 16 proposals
000318.jpg: 2 GT bboxes and 5 proposals
000321.jpg: 1 GT bboxes and 5 proposals
000322.jpg: 1 GT bboxes and 2 proposals
000323.jpg: 2 GT bboxes and 2 proposals
000328.jpg: 8 GT bboxes and 14 proposals
000329.jpg: 3 GT bboxes and 1 proposals
000332.jpg: 1 GT bboxes and 2 proposals
000336.jpg: 1 GT bboxes and 1 proposals
000338.jpg: 2 GT bboxes and 2 proposals
000340.jpg: 2 GT bboxes and 1 proposals
000343.jpg: 1 GT bboxes and 1 proposals
000352.jpg: 5 GT bboxes and 21 proposals
000354.jpg: 1 GT bboxes and 1 proposals
000363.jpg: 2 GT bboxes and 6 proposals
000373.jpg: 1 GT bboxes and 4 proposals
000374.jpg: 10 GT bboxes and 30 proposals
000380.jpg: 1 GT bboxes and 0 proposals
000381.jpg: 5 GT bboxes and 9 proposals
000396.jpg: 1 GT bboxes and 4 proposals
000403.jpg: 1 GT bboxes and 1 proposals
000408.jpg: 1 GT bboxes and 3 proposals
000417.jpg: 1 GT bboxes and 1 proposals
000419.jpg: 4 GT bboxes and 8 proposals
000420.jpg: 1 GT bboxes and 1 proposals
000424.jpg: 4 GT bboxes and 5 proposals
000427.jpg: 1 GT bboxes and 2 proposals
000428.jpg: 3 GT bboxes and 15 proposals
000433.jpg: 2 GT bboxes and 3 proposals
000435.jpg: 2 GT bboxes and 3 proposals
000439.jpg: 1 GT bboxes and 8 proposals
000443.jpg: 2 GT bboxes and 11 proposals
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002385.jpg: 11 GT bboxes and 33 proposals
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002444.jpg: 4 GT bboxes and 23 proposals
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002504.jpg: 15 GT bboxes and 13 proposals
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002513.jpg: 8 GT bboxes and 2 proposals
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002542.jpg: 12 GT bboxes and 18 proposals
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002566.jpg: 8 GT bboxes and 8 proposals
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002584.jpg: 5 GT bboxes and 13 proposals
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002589.jpg: 8 GT bboxes and 18 proposals
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002600.jpg: 1 GT bboxes and 2 proposals
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002606.jpg: 5 GT bboxes and 11 proposals
002613.jpg: 6 GT bboxes and 21 proposals
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002632.jpg: 1 GT bboxes and 1 proposals
002633.jpg: 2 GT bboxes and 1 proposals
002636.jpg: 8 GT bboxes and 25 proposals
002637.jpg: 2 GT bboxes and 3 proposals
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002643.jpg: 4 GT bboxes and 10 proposals
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002658.jpg: 8 GT bboxes and 19 proposals
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002668.jpg: 6 GT bboxes and 6 proposals
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002675.jpg: 2 GT bboxes and 6 proposals
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002678.jpg: 5 GT bboxes and 13 proposals
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002693.jpg: 6 GT bboxes and 18 proposals
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002717.jpg: 1 GT bboxes and 4 proposals
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002721.jpg: 1 GT bboxes and 6 proposals
002723.jpg: 5 GT bboxes and 11 proposals
002727.jpg: 3 GT bboxes and 9 proposals
002732.jpg: 1 GT bboxes and 1 proposals
002734.jpg: 3 GT bboxes and 6 proposals
002741.jpg: 5 GT bboxes and 18 proposals
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002751.jpg: 5 GT bboxes and 21 proposals
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002767.jpg: 7 GT bboxes and 21 proposals
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002798.jpg: 13 GT bboxes and 30 proposals
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002827.jpg: 4 GT bboxes and 17 proposals
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002943.jpg: 4 GT bboxes and 9 proposals
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002954.jpg: 2 GT bboxes and 4 proposals
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002963.jpg: 1 GT bboxes and 1 proposals
002965.jpg: 26 GT bboxes and 47 proposals
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002978.jpg: 3 GT bboxes and 10 proposals
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002986.jpg: 15 GT bboxes and 29 proposals
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003044.jpg: 5 GT bboxes and 10 proposals
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003065.jpg: 6 GT bboxes and 15 proposals
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003102.jpg: 2 GT bboxes and 5 proposals
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003129.jpg: 13 GT bboxes and 22 proposals
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003207.jpg: 10 GT bboxes and 13 proposals
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003218.jpg: 26 GT bboxes and 27 proposals
003219.jpg: 5 GT bboxes and 2 proposals
003223.jpg: 1 GT bboxes and 13 proposals
003228.jpg: 8 GT bboxes and 15 proposals
003239.jpg: 2 GT bboxes and 9 proposals
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003300.jpg: 8 GT bboxes and 12 proposals
003301.jpg: 4 GT bboxes and 4 proposals
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003311.jpg: 7 GT bboxes and 13 proposals
003313.jpg: 5 GT bboxes and 9 proposals
003316.jpg: 3 GT bboxes and 5 proposals
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003335.jpg: 7 GT bboxes and 6 proposals
003344.jpg: 12 GT bboxes and 18 proposals
003351.jpg: 1 GT bboxes and 2 proposals
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003362.jpg: 2 GT bboxes and 3 proposals
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003376.jpg: 5 GT bboxes and 15 proposals
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003407.jpg: 10 GT bboxes and 7 proposals
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003415.jpg: 2 GT bboxes and 2 proposals
003419.jpg: 20 GT bboxes and 25 proposals
003422.jpg: 5 GT bboxes and 17 proposals
003425.jpg: 1 GT bboxes and 3 proposals
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003435.jpg: 2 GT bboxes and 18 proposals
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003449.jpg: 4 GT bboxes and 3 proposals
003451.jpg: 4 GT bboxes and 19 proposals
003453.jpg: 2 GT bboxes and 10 proposals
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003461.jpg: 13 GT bboxes and 2 proposals
003462.jpg: 5 GT bboxes and 11 proposals
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003468.jpg: 2 GT bboxes and 5 proposals
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003470.jpg: 10 GT bboxes and 18 proposals
003492.jpg: 3 GT bboxes and 4 proposals
003516.jpg: 9 GT bboxes and 30 proposals
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003521.jpg: 5 GT bboxes and 12 proposals
003528.jpg: 8 GT bboxes and 8 proposals
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003536.jpg: 2 GT bboxes and 5 proposals
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003554.jpg: 1 GT bboxes and 1 proposals
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003566.jpg: 2 GT bboxes and 19 proposals
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003589.jpg: 15 GT bboxes and 22 proposals
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003618.jpg: 7 GT bboxes and 9 proposals
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003636.jpg: 10 GT bboxes and 23 proposals
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003654.jpg: 7 GT bboxes and 2 proposals
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003711.jpg: 8 GT bboxes and 11 proposals
003717.jpg: 15 GT bboxes and 23 proposals
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003754.jpg: 1 GT bboxes and 12 proposals
003760.jpg: 19 GT bboxes and 13 proposals
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003780.jpg: 6 GT bboxes and 9 proposals
003783.jpg: 25 GT bboxes and 19 proposals
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003803.jpg: 1 GT bboxes and 9 proposals
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003837.jpg: 2 GT bboxes and 5 proposals
003838.jpg: 6 GT bboxes and 9 proposals
003844.jpg: 2 GT bboxes and 12 proposals
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003846.jpg: 2 GT bboxes and 9 proposals
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003855.jpg: 10 GT bboxes and 19 proposals
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003877.jpg: 5 GT bboxes and 18 proposals
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003891.jpg: 10 GT bboxes and 22 proposals
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003919.jpg: 10 GT bboxes and 4 proposals
003923.jpg: 6 GT bboxes and 14 proposals
003924.jpg: 11 GT bboxes and 7 proposals
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003946.jpg: 2 GT bboxes and 4 proposals
003947.jpg: 4 GT bboxes and 12 proposals
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003954.jpg: 3 GT bboxes and 10 proposals
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003960.jpg: 6 GT bboxes and 14 proposals
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003973.jpg: 4 GT bboxes and 15 proposals
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003986.jpg: 3 GT bboxes and 3 proposals
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003992.jpg: 6 GT bboxes and 8 proposals
003994.jpg: 4 GT bboxes and 23 proposals
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004020.jpg: 3 GT bboxes and 4 proposals
004025.jpg: 1 GT bboxes and 5 proposals
004031.jpg: 3 GT bboxes and 2 proposals
004039.jpg: 3 GT bboxes and 6 proposals
004047.jpg: 5 GT bboxes and 6 proposals
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004057.jpg: 1 GT bboxes and 6 proposals
004060.jpg: 3 GT bboxes and 3 proposals
004066.jpg: 1 GT bboxes and 3 proposals
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004073.jpg: 4 GT bboxes and 6 proposals
004075.jpg: 3 GT bboxes and 4 proposals
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004077.jpg: 3 GT bboxes and 4 proposals
004082.jpg: 1 GT bboxes and 8 proposals
004085.jpg: 2 GT bboxes and 13 proposals
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004105.jpg: 8 GT bboxes and 29 proposals
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004122.jpg: 10 GT bboxes and 13 proposals
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004145.jpg: 14 GT bboxes and 17 proposals
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004186.jpg: 8 GT bboxes and 10 proposals
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004204.jpg: 5 GT bboxes and 21 proposals
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004212.jpg: 2 GT bboxes and 6 proposals
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004230.jpg: 2 GT bboxes and 3 proposals
004239.jpg: 2 GT bboxes and 2 proposals
004246.jpg: 2 GT bboxes and 15 proposals
004257.jpg: 3 GT bboxes and 18 proposals
004258.jpg: 10 GT bboxes and 14 proposals
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004265.jpg: 1 GT bboxes and 3 proposals
004274.jpg: 2 GT bboxes and 5 proposals
004275.jpg: 5 GT bboxes and 7 proposals
004279.jpg: 8 GT bboxes and 23 proposals
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004298.jpg: 3 GT bboxes and 8 proposals
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004310.jpg: 2 GT bboxes and 1 proposals
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004321.jpg: 2 GT bboxes and 3 proposals
004323.jpg: 4 GT bboxes and 9 proposals
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004329.jpg: 9 GT bboxes and 16 proposals
004331.jpg: 1 GT bboxes and 15 proposals
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004346.jpg: 9 GT bboxes and 16 proposals
004349.jpg: 42 GT bboxes and 22 proposals
004351.jpg: 1 GT bboxes and 0 proposals
004352.jpg: 1 GT bboxes and 1 proposals
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004364.jpg: 4 GT bboxes and 13 proposals
004368.jpg: 3 GT bboxes and 3 proposals
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004380.jpg: 3 GT bboxes and 4 proposals
004384.jpg: 2 GT bboxes and 2 proposals
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004397.jpg: 1 GT bboxes and 7 proposals
004405.jpg: 4 GT bboxes and 16 proposals
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004411.jpg: 1 GT bboxes and 2 proposals
004421.jpg: 8 GT bboxes and 24 proposals
004423.jpg: 3 GT bboxes and 3 proposals
004424.jpg: 5 GT bboxes and 10 proposals
004429.jpg: 2 GT bboxes and 2 proposals
004430.jpg: 1 GT bboxes and 3 proposals
004432.jpg: 1 GT bboxes and 1 proposals
004433.jpg: 4 GT bboxes and 2 proposals
004437.jpg: 7 GT bboxes and 2 proposals
004438.jpg: 4 GT bboxes and 9 proposals
004446.jpg: 2 GT bboxes and 4 proposals
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004455.jpg: 2 GT bboxes and 9 proposals
004457.jpg: 3 GT bboxes and 9 proposals
004459.jpg: 6 GT bboxes and 4 proposals
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004464.jpg: 3 GT bboxes and 5 proposals
004466.jpg: 12 GT bboxes and 15 proposals
004468.jpg: 1 GT bboxes and 1 proposals
004474.jpg: 3 GT bboxes and 4 proposals
004487.jpg: 2 GT bboxes and 3 proposals
004488.jpg: 3 GT bboxes and 5 proposals
004490.jpg: 1 GT bboxes and 1 proposals
004493.jpg: 2 GT bboxes and 20 proposals
004494.jpg: 4 GT bboxes and 20 proposals
004495.jpg: 4 GT bboxes and 12 proposals
004498.jpg: 1 GT bboxes and 3 proposals
004499.jpg: 4 GT bboxes and 4 proposals
004507.jpg: 1 GT bboxes and 1 proposals
004509.jpg: 2 GT bboxes and 3 proposals
004512.jpg: 6 GT bboxes and 4 proposals
004518.jpg: 1 GT bboxes and 24 proposals
004527.jpg: 19 GT bboxes and 28 proposals
004528.jpg: 2 GT bboxes and 3 proposals
004530.jpg: 2 GT bboxes and 8 proposals
004532.jpg: 1 GT bboxes and 3 proposals
004535.jpg: 2 GT bboxes and 2 proposals
004539.jpg: 7 GT bboxes and 13 proposals
004542.jpg: 5 GT bboxes and 19 proposals
004552.jpg: 1 GT bboxes and 7 proposals
004555.jpg: 1 GT bboxes and 3 proposals
004558.jpg: 4 GT bboxes and 2 proposals
004574.jpg: 2 GT bboxes and 3 proposals
004581.jpg: 3 GT bboxes and 4 proposals
004585.jpg: 13 GT bboxes and 21 proposals
004588.jpg: 9 GT bboxes and 9 proposals
004592.jpg: 1 GT bboxes and 0 proposals
004600.jpg: 1 GT bboxes and 2 proposals
004601.jpg: 2 GT bboxes and 8 proposals
004606.jpg: 1 GT bboxes and 0 proposals
004609.jpg: 2 GT bboxes and 3 proposals
004618.jpg: 1 GT bboxes and 0 proposals
004626.jpg: 2 GT bboxes and 3 proposals
004630.jpg: 9 GT bboxes and 18 proposals
004632.jpg: 2 GT bboxes and 1 proposals
004647.jpg: 3 GT bboxes and 4 proposals
004649.jpg: 3 GT bboxes and 1 proposals
004652.jpg: 1 GT bboxes and 2 proposals
004653.jpg: 1 GT bboxes and 2 proposals
004654.jpg: 2 GT bboxes and 1 proposals
004655.jpg: 11 GT bboxes and 35 proposals
004660.jpg: 4 GT bboxes and 1 proposals
004662.jpg: 2 GT bboxes and 4 proposals
004672.jpg: 2 GT bboxes and 2 proposals
004673.jpg: 2 GT bboxes and 9 proposals
004674.jpg: 1 GT bboxes and 3 proposals
004676.jpg: 2 GT bboxes and 8 proposals
004682.jpg: 2 GT bboxes and 14 proposals
004689.jpg: 1 GT bboxes and 1 proposals
004692.jpg: 4 GT bboxes and 8 proposals
004699.jpg: 8 GT bboxes and 5 proposals
004707.jpg: 2 GT bboxes and 4 proposals
004708.jpg: 3 GT bboxes and 1 proposals
004719.jpg: 2 GT bboxes and 4 proposals
004722.jpg: 3 GT bboxes and 5 proposals
004727.jpg: 13 GT bboxes and 12 proposals
004732.jpg: 2 GT bboxes and 12 proposals
004746.jpg: 2 GT bboxes and 2 proposals
004750.jpg: 3 GT bboxes and 10 proposals
004761.jpg: 8 GT bboxes and 19 proposals
004768.jpg: 1 GT bboxes and 2 proposals
004770.jpg: 1 GT bboxes and 3 proposals
004777.jpg: 1 GT bboxes and 3 proposals
004785.jpg: 12 GT bboxes and 10 proposals
004786.jpg: 1 GT bboxes and 8 proposals
004788.jpg: 2 GT bboxes and 7 proposals
004789.jpg: 2 GT bboxes and 2 proposals
004796.jpg: 3 GT bboxes and 9 proposals
004805.jpg: 4 GT bboxes and 11 proposals
004812.jpg: 3 GT bboxes and 3 proposals
004814.jpg: 1 GT bboxes and 2 proposals
004816.jpg: 1 GT bboxes and 1 proposals
004818.jpg: 2 GT bboxes and 5 proposals
004825.jpg: 1 GT bboxes and 19 proposals
004826.jpg: 3 GT bboxes and 16 proposals
004831.jpg: 1 GT bboxes and 2 proposals
004834.jpg: 2 GT bboxes and 3 proposals
004839.jpg: 2 GT bboxes and 11 proposals
004840.jpg: 5 GT bboxes and 17 proposals
004850.jpg: 1 GT bboxes and 6 proposals
004852.jpg: 2 GT bboxes and 1 proposals
004856.jpg: 1 GT bboxes and 1 proposals
004859.jpg: 5 GT bboxes and 14 proposals
004863.jpg: 1 GT bboxes and 1 proposals
004866.jpg: 2 GT bboxes and 1 proposals
004867.jpg: 1 GT bboxes and 1 proposals
004868.jpg: 4 GT bboxes and 4 proposals
004872.jpg: 2 GT bboxes and 6 proposals
004878.jpg: 2 GT bboxes and 6 proposals
004886.jpg: 4 GT bboxes and 4 proposals
004890.jpg: 1 GT bboxes and 1 proposals
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004896.jpg: 1 GT bboxes and 2 proposals
004903.jpg: 4 GT bboxes and 10 proposals
004912.jpg: 3 GT bboxes and 5 proposals
004916.jpg: 3 GT bboxes and 12 proposals
004926.jpg: 4 GT bboxes and 17 proposals
004928.jpg: 3 GT bboxes and 7 proposals
004931.jpg: 4 GT bboxes and 7 proposals
004935.jpg: 2 GT bboxes and 2 proposals
004936.jpg: 1 GT bboxes and 0 proposals
004938.jpg: 1 GT bboxes and 1 proposals
004939.jpg: 3 GT bboxes and 1 proposals
004943.jpg: 7 GT bboxes and 19 proposals
004948.jpg: 4 GT bboxes and 13 proposals
004950.jpg: 2 GT bboxes and 2 proposals
004953.jpg: 18 GT bboxes and 20 proposals
004954.jpg: 2 GT bboxes and 3 proposals
004956.jpg: 2 GT bboxes and 2 proposals
004960.jpg: 3 GT bboxes and 10 proposals
004963.jpg: 1 GT bboxes and 1 proposals
004967.jpg: 1 GT bboxes and 9 proposals
004977.jpg: 2 GT bboxes and 1 proposals
004982.jpg: 1 GT bboxes and 1 proposals
004983.jpg: 2 GT bboxes and 4 proposals
004985.jpg: 2 GT bboxes and 2 proposals
004986.jpg: 2 GT bboxes and 0 proposals
004994.jpg: 2 GT bboxes and 7 proposals
004997.jpg: 2 GT bboxes and 1 proposals
004998.jpg: 1 GT bboxes and 1 proposals
004999.jpg: 1 GT bboxes and 3 proposals
005003.jpg: 1 GT bboxes and 10 proposals
005014.jpg: 8 GT bboxes and 9 proposals
005028.jpg: 2 GT bboxes and 5 proposals
005036.jpg: 5 GT bboxes and 5 proposals
005037.jpg: 1 GT bboxes and 2 proposals
005039.jpg: 7 GT bboxes and 33 proposals
005042.jpg: 4 GT bboxes and 10 proposals
005054.jpg: 2 GT bboxes and 2 proposals
005055.jpg: 6 GT bboxes and 17 proposals
005056.jpg: 1 GT bboxes and 2 proposals
005062.jpg: 1 GT bboxes and 2 proposals
005063.jpg: 2 GT bboxes and 11 proposals
005064.jpg: 2 GT bboxes and 8 proposals
005067.jpg: 1 GT bboxes and 7 proposals
005072.jpg: 1 GT bboxes and 1 proposals
005077.jpg: 1 GT bboxes and 1 proposals
005079.jpg: 1 GT bboxes and 1 proposals
005081.jpg: 12 GT bboxes and 33 proposals
005085.jpg: 3 GT bboxes and 13 proposals
005102.jpg: 6 GT bboxes and 13 proposals
005104.jpg: 3 GT bboxes and 18 proposals
005110.jpg: 6 GT bboxes and 11 proposals
005111.jpg: 1 GT bboxes and 2 proposals
005116.jpg: 11 GT bboxes and 11 proposals
005128.jpg: 1 GT bboxes and 12 proposals
005131.jpg: 1 GT bboxes and 1 proposals
005135.jpg: 2 GT bboxes and 10 proposals
005136.jpg: 1 GT bboxes and 2 proposals
005144.jpg: 1 GT bboxes and 2 proposals
005145.jpg: 2 GT bboxes and 12 proposals
005146.jpg: 10 GT bboxes and 33 proposals
005150.jpg: 3 GT bboxes and 12 proposals
005159.jpg: 3 GT bboxes and 6 proposals
005160.jpg: 13 GT bboxes and 29 proposals
005161.jpg: 12 GT bboxes and 17 proposals
005175.jpg: 5 GT bboxes and 17 proposals
005176.jpg: 4 GT bboxes and 5 proposals
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005195.jpg: 8 GT bboxes and 9 proposals
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005212.jpg: 3 GT bboxes and 4 proposals
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005220.jpg: 3 GT bboxes and 13 proposals
005222.jpg: 2 GT bboxes and 3 proposals
005224.jpg: 2 GT bboxes and 12 proposals
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005230.jpg: 7 GT bboxes and 13 proposals
005239.jpg: 4 GT bboxes and 17 proposals
005242.jpg: 5 GT bboxes and 36 proposals
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005278.jpg: 8 GT bboxes and 19 proposals
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005320.jpg: 1 GT bboxes and 9 proposals
005325.jpg: 1 GT bboxes and 13 proposals
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005331.jpg: 5 GT bboxes and 7 proposals
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005343.jpg: 2 GT bboxes and 9 proposals
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005438.jpg: 2 GT bboxes and 3 proposals
005439.jpg: 4 GT bboxes and 3 proposals
005441.jpg: 8 GT bboxes and 27 proposals
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005485.jpg: 23 GT bboxes and 14 proposals
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005507.jpg: 7 GT bboxes and 18 proposals
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005517.jpg: 5 GT bboxes and 31 proposals
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005522.jpg: 11 GT bboxes and 23 proposals
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005531.jpg: 6 GT bboxes and 19 proposals
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005554.jpg: 9 GT bboxes and 20 proposals
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005618.jpg: 6 GT bboxes and 8 proposals
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005629.jpg: 5 GT bboxes and 21 proposals
005640.jpg: 1 GT bboxes and 5 proposals
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005647.jpg: 7 GT bboxes and 20 proposals
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005701.jpg: 7 GT bboxes and 15 proposals
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005714.jpg: 3 GT bboxes and 8 proposals
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005719.jpg: 8 GT bboxes and 36 proposals
005723.jpg: 8 GT bboxes and 24 proposals
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005732.jpg: 6 GT bboxes and 14 proposals
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005741.jpg: 2 GT bboxes and 5 proposals
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005760.jpg: 2 GT bboxes and 9 proposals
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005762.jpg: 2 GT bboxes and 11 proposals
005768.jpg: 4 GT bboxes and 8 proposals
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005812.jpg: 2 GT bboxes and 6 proposals
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005818.jpg: 2 GT bboxes and 10 proposals
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005828.jpg: 1 GT bboxes and 3 proposals
005829.jpg: 3 GT bboxes and 11 proposals
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005863.jpg: 13 GT bboxes and 18 proposals
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005914.jpg: 3 GT bboxes and 7 proposals
005917.jpg: 3 GT bboxes and 4 proposals
005919.jpg: 20 GT bboxes and 31 proposals
005928.jpg: 7 GT bboxes and 26 proposals
005940.jpg: 6 GT bboxes and 15 proposals
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005963.jpg: 4 GT bboxes and 21 proposals
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006018.jpg: 10 GT bboxes and 16 proposals
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006028.jpg: 3 GT bboxes and 17 proposals
006029.jpg: 11 GT bboxes and 22 proposals
006035.jpg: 6 GT bboxes and 11 proposals
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006045.jpg: 2 GT bboxes and 10 proposals
006046.jpg: 3 GT bboxes and 19 proposals
006055.jpg: 5 GT bboxes and 24 proposals
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006071.jpg: 10 GT bboxes and 19 proposals
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006133.jpg: 17 GT bboxes and 10 proposals
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006150.jpg: 4 GT bboxes and 18 proposals
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006161.jpg: 11 GT bboxes and 39 proposals
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006185.jpg: 5 GT bboxes and 15 proposals
006188.jpg: 4 GT bboxes and 22 proposals
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006198.jpg: 10 GT bboxes and 26 proposals
006201.jpg: 2 GT bboxes and 10 proposals
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006206.jpg: 3 GT bboxes and 15 proposals
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006216.jpg: 4 GT bboxes and 1 proposals
006218.jpg: 2 GT bboxes and 9 proposals
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006220.jpg: 8 GT bboxes and 21 proposals
006222.jpg: 2 GT bboxes and 7 proposals
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006235.jpg: 11 GT bboxes and 6 proposals
006240.jpg: 1 GT bboxes and 2 proposals
006241.jpg: 7 GT bboxes and 28 proposals
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006259.jpg: 6 GT bboxes and 36 proposals
006260.jpg: 1 GT bboxes and 2 proposals
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006286.jpg: 20 GT bboxes and 20 proposals
006295.jpg: 3 GT bboxes and 8 proposals
006296.jpg: 3 GT bboxes and 8 proposals
006300.jpg: 6 GT bboxes and 13 proposals
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006314.jpg: 1 GT bboxes and 2 proposals
006318.jpg: 1 GT bboxes and 2 proposals
006319.jpg: 2 GT bboxes and 8 proposals
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006323.jpg: 2 GT bboxes and 23 proposals
006325.jpg: 1 GT bboxes and 4 proposals
006330.jpg: 8 GT bboxes and 15 proposals
006335.jpg: 1 GT bboxes and 7 proposals
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006339.jpg: 8 GT bboxes and 4 proposals
006346.jpg: 11 GT bboxes and 24 proposals
006348.jpg: 2 GT bboxes and 7 proposals
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006391.jpg: 2 GT bboxes and 10 proposals
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006396.jpg: 9 GT bboxes and 13 proposals
006398.jpg: 16 GT bboxes and 32 proposals
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006409.jpg: 4 GT bboxes and 12 proposals
006421.jpg: 3 GT bboxes and 11 proposals
006424.jpg: 2 GT bboxes and 4 proposals
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006428.jpg: 3 GT bboxes and 4 proposals
006430.jpg: 1 GT bboxes and 8 proposals
006437.jpg: 1 GT bboxes and 10 proposals
006440.jpg: 2 GT bboxes and 9 proposals
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006450.jpg: 3 GT bboxes and 7 proposals
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006465.jpg: 8 GT bboxes and 15 proposals
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006473.jpg: 3 GT bboxes and 5 proposals
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006484.jpg: 1 GT bboxes and 6 proposals
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006497.jpg: 8 GT bboxes and 8 proposals
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006519.jpg: 6 GT bboxes and 7 proposals
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006529.jpg: 2 GT bboxes and 2 proposals
006530.jpg: 6 GT bboxes and 19 proposals
006532.jpg: 2 GT bboxes and 3 proposals
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006538.jpg: 1 GT bboxes and 1 proposals
006542.jpg: 16 GT bboxes and 9 proposals
006543.jpg: 1 GT bboxes and 7 proposals
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006562.jpg: 5 GT bboxes and 8 proposals
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006570.jpg: 2 GT bboxes and 8 proposals
006572.jpg: 5 GT bboxes and 17 proposals
006575.jpg: 1 GT bboxes and 9 proposals
006576.jpg: 5 GT bboxes and 9 proposals
006578.jpg: 3 GT bboxes and 9 proposals
006583.jpg: 3 GT bboxes and 14 proposals
006584.jpg: 10 GT bboxes and 30 proposals
006585.jpg: 2 GT bboxes and 5 proposals
006587.jpg: 1 GT bboxes and 2 proposals
006588.jpg: 7 GT bboxes and 4 proposals
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006599.jpg: 4 GT bboxes and 9 proposals
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006611.jpg: 2 GT bboxes and 1 proposals
006617.jpg: 2 GT bboxes and 4 proposals
006618.jpg: 2 GT bboxes and 9 proposals
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006621.jpg: 2 GT bboxes and 6 proposals
006625.jpg: 6 GT bboxes and 12 proposals
006628.jpg: 11 GT bboxes and 22 proposals
006631.jpg: 2 GT bboxes and 2 proposals
006632.jpg: 2 GT bboxes and 6 proposals
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006645.jpg: 2 GT bboxes and 2 proposals
006647.jpg: 4 GT bboxes and 7 proposals
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006661.jpg: 12 GT bboxes and 9 proposals
006664.jpg: 2 GT bboxes and 4 proposals
006666.jpg: 2 GT bboxes and 5 proposals
006667.jpg: 2 GT bboxes and 2 proposals
006668.jpg: 3 GT bboxes and 8 proposals
006670.jpg: 3 GT bboxes and 7 proposals
006671.jpg: 2 GT bboxes and 10 proposals
006673.jpg: 12 GT bboxes and 21 proposals
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006682.jpg: 3 GT bboxes and 4 proposals
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006696.jpg: 7 GT bboxes and 26 proposals
006699.jpg: 2 GT bboxes and 5 proposals
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006709.jpg: 3 GT bboxes and 11 proposals
006718.jpg: 2 GT bboxes and 6 proposals
006719.jpg: 5 GT bboxes and 13 proposals
006722.jpg: 6 GT bboxes and 11 proposals
006725.jpg: 1 GT bboxes and 1 proposals
006730.jpg: 1 GT bboxes and 1 proposals
006739.jpg: 3 GT bboxes and 3 proposals
006747.jpg: 1 GT bboxes and 7 proposals
006751.jpg: 2 GT bboxes and 4 proposals
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006760.jpg: 3 GT bboxes and 1 proposals
006761.jpg: 1 GT bboxes and 3 proposals
006762.jpg: 3 GT bboxes and 8 proposals
006765.jpg: 5 GT bboxes and 16 proposals
006768.jpg: 4 GT bboxes and 11 proposals
006769.jpg: 2 GT bboxes and 2 proposals
006772.jpg: 2 GT bboxes and 4 proposals
006783.jpg: 6 GT bboxes and 9 proposals
006786.jpg: 2 GT bboxes and 6 proposals
006789.jpg: 3 GT bboxes and 6 proposals
006797.jpg: 2 GT bboxes and 2 proposals
006799.jpg: 1 GT bboxes and 2 proposals
006800.jpg: 6 GT bboxes and 13 proposals
006802.jpg: 3 GT bboxes and 3 proposals
006803.jpg: 3 GT bboxes and 5 proposals
006808.jpg: 1 GT bboxes and 4 proposals
006813.jpg: 2 GT bboxes and 2 proposals
006814.jpg: 1 GT bboxes and 6 proposals
006819.jpg: 5 GT bboxes and 13 proposals
006821.jpg: 6 GT bboxes and 16 proposals
006827.jpg: 2 GT bboxes and 7 proposals
006828.jpg: 1 GT bboxes and 5 proposals
006829.jpg: 1 GT bboxes and 1 proposals
006835.jpg: 2 GT bboxes and 4 proposals
006838.jpg: 1 GT bboxes and 1 proposals
006841.jpg: 7 GT bboxes and 19 proposals
006842.jpg: 2 GT bboxes and 9 proposals
006850.jpg: 2 GT bboxes and 5 proposals
006855.jpg: 3 GT bboxes and 5 proposals
006859.jpg: 2 GT bboxes and 3 proposals
006860.jpg: 1 GT bboxes and 10 proposals
006862.jpg: 7 GT bboxes and 10 proposals
006865.jpg: 1 GT bboxes and 1 proposals
006867.jpg: 1 GT bboxes and 0 proposals
006876.jpg: 3 GT bboxes and 4 proposals
006878.jpg: 2 GT bboxes and 2 proposals
006880.jpg: 6 GT bboxes and 12 proposals
006884.jpg: 1 GT bboxes and 1 proposals
006886.jpg: 2 GT bboxes and 9 proposals
006892.jpg: 6 GT bboxes and 15 proposals
006903.jpg: 4 GT bboxes and 6 proposals
006908.jpg: 13 GT bboxes and 23 proposals
006918.jpg: 2 GT bboxes and 4 proposals
006922.jpg: 1 GT bboxes and 6 proposals
006924.jpg: 1 GT bboxes and 1 proposals
006932.jpg: 2 GT bboxes and 4 proposals
006933.jpg: 2 GT bboxes and 3 proposals
006934.jpg: 6 GT bboxes and 8 proposals
006935.jpg: 1 GT bboxes and 1 proposals
006940.jpg: 1 GT bboxes and 1 proposals
006944.jpg: 6 GT bboxes and 15 proposals
006945.jpg: 6 GT bboxes and 10 proposals
006949.jpg: 1 GT bboxes and 1 proposals
006952.jpg: 1 GT bboxes and 0 proposals
006953.jpg: 1 GT bboxes and 7 proposals
006956.jpg: 4 GT bboxes and 24 proposals
006962.jpg: 1 GT bboxes and 10 proposals
006963.jpg: 5 GT bboxes and 10 proposals
006965.jpg: 6 GT bboxes and 2 proposals
006966.jpg: 1 GT bboxes and 3 proposals
006972.jpg: 4 GT bboxes and 22 proposals
006981.jpg: 1 GT bboxes and 13 proposals
006987.jpg: 3 GT bboxes and 10 proposals
006988.jpg: 2 GT bboxes and 3 proposals
006989.jpg: 2 GT bboxes and 2 proposals
006990.jpg: 2 GT bboxes and 6 proposals
006994.jpg: 1 GT bboxes and 3 proposals
006995.jpg: 1 GT bboxes and 10 proposals
007004.jpg: 6 GT bboxes and 13 proposals
007008.jpg: 2 GT bboxes and 2 proposals
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007020.jpg: 1 GT bboxes and 4 proposals
007021.jpg: 5 GT bboxes and 17 proposals
007022.jpg: 1 GT bboxes and 4 proposals
007031.jpg: 2 GT bboxes and 3 proposals
007035.jpg: 3 GT bboxes and 9 proposals
007038.jpg: 1 GT bboxes and 1 proposals
007042.jpg: 6 GT bboxes and 10 proposals
007046.jpg: 2 GT bboxes and 5 proposals
007048.jpg: 7 GT bboxes and 0 proposals
007049.jpg: 3 GT bboxes and 6 proposals
007052.jpg: 3 GT bboxes and 9 proposals
007054.jpg: 1 GT bboxes and 5 proposals
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007058.jpg: 1 GT bboxes and 4 proposals
007059.jpg: 10 GT bboxes and 13 proposals
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007068.jpg: 8 GT bboxes and 1 proposals
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007074.jpg: 3 GT bboxes and 9 proposals
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007086.jpg: 1 GT bboxes and 4 proposals
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007100.jpg: 2 GT bboxes and 6 proposals
007101.jpg: 1 GT bboxes and 1 proposals
007104.jpg: 1 GT bboxes and 1 proposals
007109.jpg: 2 GT bboxes and 3 proposals
007114.jpg: 3 GT bboxes and 4 proposals
007117.jpg: 9 GT bboxes and 7 proposals
007122.jpg: 2 GT bboxes and 1 proposals
007123.jpg: 1 GT bboxes and 2 proposals
007132.jpg: 2 GT bboxes and 2 proposals
007139.jpg: 2 GT bboxes and 16 proposals
007140.jpg: 2 GT bboxes and 9 proposals
007141.jpg: 3 GT bboxes and 7 proposals
007144.jpg: 1 GT bboxes and 13 proposals
007146.jpg: 1 GT bboxes and 1 proposals
007147.jpg: 2 GT bboxes and 9 proposals
007148.jpg: 2 GT bboxes and 3 proposals
007149.jpg: 1 GT bboxes and 1 proposals
007153.jpg: 3 GT bboxes and 8 proposals
007162.jpg: 1 GT bboxes and 5 proposals
007165.jpg: 2 GT bboxes and 4 proposals
007167.jpg: 4 GT bboxes and 2 proposals
007172.jpg: 8 GT bboxes and 12 proposals
007174.jpg: 1 GT bboxes and 10 proposals
007187.jpg: 4 GT bboxes and 9 proposals
007189.jpg: 2 GT bboxes and 1 proposals
007191.jpg: 12 GT bboxes and 22 proposals
007200.jpg: 2 GT bboxes and 6 proposals
007204.jpg: 2 GT bboxes and 1 proposals
007208.jpg: 3 GT bboxes and 3 proposals
007210.jpg: 1 GT bboxes and 5 proposals
007211.jpg: 1 GT bboxes and 4 proposals
007212.jpg: 3 GT bboxes and 1 proposals
007215.jpg: 1 GT bboxes and 1 proposals
007216.jpg: 2 GT bboxes and 3 proposals
007217.jpg: 11 GT bboxes and 27 proposals
007224.jpg: 1 GT bboxes and 2 proposals
007227.jpg: 2 GT bboxes and 4 proposals
007230.jpg: 2 GT bboxes and 6 proposals
007236.jpg: 6 GT bboxes and 6 proposals
007244.jpg: 2 GT bboxes and 6 proposals
007245.jpg: 2 GT bboxes and 3 proposals
007247.jpg: 2 GT bboxes and 4 proposals
007249.jpg: 1 GT bboxes and 1 proposals
007258.jpg: 1 GT bboxes and 0 proposals
007259.jpg: 1 GT bboxes and 1 proposals
007260.jpg: 2 GT bboxes and 3 proposals
007266.jpg: 5 GT bboxes and 4 proposals
007270.jpg: 3 GT bboxes and 10 proposals
007274.jpg: 1 GT bboxes and 2 proposals
007275.jpg: 1 GT bboxes and 1 proposals
007276.jpg: 2 GT bboxes and 1 proposals
007280.jpg: 2 GT bboxes and 5 proposals
007283.jpg: 3 GT bboxes and 9 proposals
007284.jpg: 2 GT bboxes and 2 proposals
007292.jpg: 2 GT bboxes and 10 proposals
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007435.jpg: 5 GT bboxes and 20 proposals
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007443.jpg: 5 GT bboxes and 18 proposals
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007727.jpg: 4 GT bboxes and 24 proposals
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007799.jpg: 16 GT bboxes and 30 proposals
007812.jpg: 2 GT bboxes and 20 proposals
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007826.jpg: 7 GT bboxes and 29 proposals
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007943.jpg: 3 GT bboxes and 21 proposals
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007970.jpg: 3 GT bboxes and 10 proposals
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008048.jpg: 5 GT bboxes and 29 proposals
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008069.jpg: 16 GT bboxes and 25 proposals
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008105.jpg: 23 GT bboxes and 19 proposals
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008190.jpg: 9 GT bboxes and 25 proposals
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008224.jpg: 7 GT bboxes and 20 proposals
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008319.jpg: 28 GT bboxes and 42 proposals
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008359.jpg: 4 GT bboxes and 19 proposals
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008376.jpg: 5 GT bboxes and 9 proposals
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008410.jpg: 6 GT bboxes and 18 proposals
008413.jpg: 4 GT bboxes and 5 proposals
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008424.jpg: 6 GT bboxes and 14 proposals
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008430.jpg: 1 GT bboxes and 2 proposals
008433.jpg: 6 GT bboxes and 12 proposals
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008444.jpg: 15 GT bboxes and 23 proposals
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008526.jpg: 5 GT bboxes and 22 proposals
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008592.jpg: 8 GT bboxes and 15 proposals
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008612.jpg: 2 GT bboxes and 6 proposals
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008624.jpg: 6 GT bboxes and 23 proposals
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008639.jpg: 3 GT bboxes and 13 proposals
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008680.jpg: 5 GT bboxes and 20 proposals
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008717.jpg: 7 GT bboxes and 20 proposals
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008728.jpg: 3 GT bboxes and 6 proposals
008730.jpg: 5 GT bboxes and 16 proposals
008733.jpg: 6 GT bboxes and 17 proposals
008739.jpg: 8 GT bboxes and 7 proposals
008742.jpg: 5 GT bboxes and 10 proposals
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008769.jpg: 2 GT bboxes and 5 proposals
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008775.jpg: 12 GT bboxes and 16 proposals
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008801.jpg: 5 GT bboxes and 11 proposals
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008817.jpg: 2 GT bboxes and 18 proposals
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008831.jpg: 2 GT bboxes and 9 proposals
008833.jpg: 2 GT bboxes and 13 proposals
008835.jpg: 7 GT bboxes and 5 proposals
008836.jpg: 15 GT bboxes and 17 proposals
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008848.jpg: 4 GT bboxes and 13 proposals
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008892.jpg: 8 GT bboxes and 18 proposals
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008927.jpg: 1 GT bboxes and 3 proposals
008931.jpg: 8 GT bboxes and 21 proposals
008940.jpg: 1 GT bboxes and 1 proposals
008942.jpg: 9 GT bboxes and 18 proposals
008943.jpg: 15 GT bboxes and 12 proposals
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008983.jpg: 8 GT bboxes and 24 proposals
008997.jpg: 11 GT bboxes and 23 proposals
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009015.jpg: 6 GT bboxes and 10 proposals
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009024.jpg: 2 GT bboxes and 2 proposals
009034.jpg: 15 GT bboxes and 26 proposals
009035.jpg: 1 GT bboxes and 1 proposals
009037.jpg: 1 GT bboxes and 7 proposals
009039.jpg: 2 GT bboxes and 1 proposals
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009051.jpg: 6 GT bboxes and 16 proposals
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009060.jpg: 2 GT bboxes and 3 proposals
009064.jpg: 9 GT bboxes and 17 proposals
009072.jpg: 1 GT bboxes and 9 proposals
009079.jpg: 4 GT bboxes and 3 proposals
009085.jpg: 6 GT bboxes and 13 proposals
009087.jpg: 8 GT bboxes and 21 proposals
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009091.jpg: 11 GT bboxes and 17 proposals
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009112.jpg: 1 GT bboxes and 1 proposals
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009126.jpg: 1 GT bboxes and 12 proposals
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009131.jpg: 1 GT bboxes and 10 proposals
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009141.jpg: 6 GT bboxes and 12 proposals
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009151.jpg: 4 GT bboxes and 14 proposals
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009163.jpg: 4 GT bboxes and 19 proposals
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009177.jpg: 4 GT bboxes and 16 proposals
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009179.jpg: 4 GT bboxes and 12 proposals
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009189.jpg: 6 GT bboxes and 16 proposals
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009193.jpg: 4 GT bboxes and 3 proposals
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009224.jpg: 5 GT bboxes and 5 proposals
009236.jpg: 2 GT bboxes and 5 proposals
009239.jpg: 3 GT bboxes and 11 proposals
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009247.jpg: 5 GT bboxes and 39 proposals
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009273.jpg: 6 GT bboxes and 2 proposals
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009279.jpg: 4 GT bboxes and 17 proposals
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009286.jpg: 8 GT bboxes and 16 proposals
009291.jpg: 5 GT bboxes and 14 proposals
009303.jpg: 7 GT bboxes and 26 proposals
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009312.jpg: 2 GT bboxes and 16 proposals
009315.jpg: 2 GT bboxes and 13 proposals
009323.jpg: 3 GT bboxes and 6 proposals
009326.jpg: 1 GT bboxes and 8 proposals
009330.jpg: 2 GT bboxes and 2 proposals
009331.jpg: 5 GT bboxes and 7 proposals
009334.jpg: 1 GT bboxes and 3 proposals
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009347.jpg: 2 GT bboxes and 3 proposals
009348.jpg: 1 GT bboxes and 6 proposals
009349.jpg: 6 GT bboxes and 22 proposals
009350.jpg: 1 GT bboxes and 13 proposals
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009354.jpg: 1 GT bboxes and 2 proposals
009368.jpg: 16 GT bboxes and 21 proposals
009371.jpg: 1 GT bboxes and 2 proposals
009373.jpg: 2 GT bboxes and 8 proposals
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009375.jpg: 3 GT bboxes and 6 proposals
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009382.jpg: 3 GT bboxes and 6 proposals
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009408.jpg: 1 GT bboxes and 3 proposals
009412.jpg: 3 GT bboxes and 2 proposals
009414.jpg: 4 GT bboxes and 2 proposals
009433.jpg: 7 GT bboxes and 4 proposals
009437.jpg: 4 GT bboxes and 26 proposals
009438.jpg: 1 GT bboxes and 1 proposals
009439.jpg: 1 GT bboxes and 4 proposals
009440.jpg: 2 GT bboxes and 8 proposals
009443.jpg: 3 GT bboxes and 2 proposals
009445.jpg: 1 GT bboxes and 7 proposals
009448.jpg: 7 GT bboxes and 7 proposals
009454.jpg: 2 GT bboxes and 24 proposals
009455.jpg: 1 GT bboxes and 1 proposals
009456.jpg: 6 GT bboxes and 15 proposals
009457.jpg: 1 GT bboxes and 2 proposals
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009461.jpg: 2 GT bboxes and 4 proposals
009464.jpg: 1 GT bboxes and 1 proposals
009468.jpg: 2 GT bboxes and 7 proposals
009470.jpg: 3 GT bboxes and 2 proposals
009472.jpg: 1 GT bboxes and 4 proposals
009477.jpg: 4 GT bboxes and 14 proposals
009479.jpg: 3 GT bboxes and 3 proposals
009480.jpg: 3 GT bboxes and 2 proposals
009481.jpg: 12 GT bboxes and 20 proposals
009484.jpg: 3 GT bboxes and 1 proposals
009494.jpg: 2 GT bboxes and 5 proposals
009500.jpg: 4 GT bboxes and 12 proposals
009502.jpg: 1 GT bboxes and 2 proposals
009507.jpg: 4 GT bboxes and 6 proposals
009517.jpg: 3 GT bboxes and 13 proposals
009519.jpg: 4 GT bboxes and 10 proposals
009527.jpg: 3 GT bboxes and 0 proposals
009531.jpg: 1 GT bboxes and 0 proposals
009532.jpg: 6 GT bboxes and 12 proposals
009533.jpg: 2 GT bboxes and 10 proposals
009540.jpg: 2 GT bboxes and 7 proposals
009543.jpg: 6 GT bboxes and 12 proposals
009546.jpg: 3 GT bboxes and 4 proposals
009550.jpg: 10 GT bboxes and 18 proposals
009558.jpg: 10 GT bboxes and 1 proposals
009560.jpg: 1 GT bboxes and 1 proposals
009565.jpg: 2 GT bboxes and 9 proposals
009567.jpg: 2 GT bboxes and 3 proposals
009568.jpg: 2 GT bboxes and 6 proposals
009571.jpg: 1 GT bboxes and 2 proposals
009580.jpg: 1 GT bboxes and 1 proposals
009586.jpg: 1 GT bboxes and 7 proposals
009588.jpg: 1 GT bboxes and 1 proposals
009591.jpg: 4 GT bboxes and 8 proposals
009597.jpg: 1 GT bboxes and 1 proposals
009598.jpg: 2 GT bboxes and 6 proposals
009603.jpg: 2 GT bboxes and 2 proposals
009611.jpg: 2 GT bboxes and 0 proposals
009617.jpg: 24 GT bboxes and 30 proposals
009619.jpg: 1 GT bboxes and 2 proposals
009620.jpg: 2 GT bboxes and 3 proposals
009627.jpg: 1 GT bboxes and 3 proposals
009636.jpg: 1 GT bboxes and 12 proposals
009641.jpg: 5 GT bboxes and 8 proposals
009647.jpg: 9 GT bboxes and 16 proposals
009649.jpg: 9 GT bboxes and 19 proposals
009655.jpg: 2 GT bboxes and 1 proposals
009658.jpg: 1 GT bboxes and 2 proposals
009667.jpg: 1 GT bboxes and 2 proposals
009670.jpg: 2 GT bboxes and 3 proposals
009676.jpg: 3 GT bboxes and 6 proposals
009678.jpg: 1 GT bboxes and 6 proposals
009681.jpg: 1 GT bboxes and 1 proposals
009685.jpg: 1 GT bboxes and 9 proposals
009686.jpg: 8 GT bboxes and 12 proposals
009687.jpg: 4 GT bboxes and 13 proposals
009692.jpg: 2 GT bboxes and 6 proposals
009695.jpg: 1 GT bboxes and 2 proposals
009698.jpg: 4 GT bboxes and 3 proposals
009699.jpg: 4 GT bboxes and 17 proposals
009700.jpg: 1 GT bboxes and 1 proposals
009706.jpg: 1 GT bboxes and 3 proposals
009710.jpg: 1 GT bboxes and 2 proposals
009711.jpg: 6 GT bboxes and 19 proposals
009712.jpg: 2 GT bboxes and 2 proposals
009719.jpg: 2 GT bboxes and 1 proposals
009724.jpg: 1 GT bboxes and 1 proposals
009726.jpg: 8 GT bboxes and 22 proposals
009732.jpg: 2 GT bboxes and 4 proposals
009737.jpg: 2 GT bboxes and 5 proposals
009738.jpg: 1 GT bboxes and 4 proposals
009743.jpg: 1 GT bboxes and 2 proposals
009745.jpg: 2 GT bboxes and 7 proposals
009746.jpg: 3 GT bboxes and 3 proposals
009747.jpg: 3 GT bboxes and 5 proposals
009748.jpg: 1 GT bboxes and 1 proposals
009754.jpg: 1 GT bboxes and 3 proposals
009758.jpg: 20 GT bboxes and 43 proposals
009761.jpg: 1 GT bboxes and 4 proposals
009764.jpg: 2 GT bboxes and 9 proposals
009767.jpg: 2 GT bboxes and 3 proposals
009772.jpg: 1 GT bboxes and 2 proposals
009773.jpg: 4 GT bboxes and 11 proposals
009778.jpg: 1 GT bboxes and 4 proposals
009780.jpg: 3 GT bboxes and 2 proposals
009781.jpg: 2 GT bboxes and 2 proposals
009785.jpg: 1 GT bboxes and 1 proposals
009794.jpg: 1 GT bboxes and 1 proposals
009796.jpg: 4 GT bboxes and 17 proposals
009801.jpg: 4 GT bboxes and 8 proposals
009809.jpg: 1 GT bboxes and 1 proposals
009816.jpg: 9 GT bboxes and 12 proposals
009819.jpg: 3 GT bboxes and 1 proposals
009822.jpg: 34 GT bboxes and 32 proposals
009823.jpg: 1 GT bboxes and 3 proposals
009831.jpg: 2 GT bboxes and 7 proposals
009833.jpg: 9 GT bboxes and 33 proposals
009836.jpg: 1 GT bboxes and 2 proposals
009841.jpg: 2 GT bboxes and 6 proposals
009858.jpg: 1 GT bboxes and 3 proposals
009862.jpg: 1 GT bboxes and 1 proposals
009863.jpg: 2 GT bboxes and 2 proposals
009865.jpg: 4 GT bboxes and 15 proposals
009870.jpg: 2 GT bboxes and 3 proposals
009880.jpg: 5 GT bboxes and 6 proposals
009881.jpg: 3 GT bboxes and 2 proposals
009886.jpg: 1 GT bboxes and 1 proposals
009894.jpg: 1 GT bboxes and 5 proposals
009897.jpg: 2 GT bboxes and 2 proposals
009898.jpg: 1 GT bboxes and 1 proposals
009900.jpg: 5 GT bboxes and 8 proposals
009902.jpg: 6 GT bboxes and 13 proposals
009905.jpg: 2 GT bboxes and 2 proposals
009908.jpg: 5 GT bboxes and 5 proposals
009913.jpg: 1 GT bboxes and 4 proposals
009917.jpg: 6 GT bboxes and 10 proposals
009923.jpg: 1 GT bboxes and 5 proposals
009932.jpg: 1 GT bboxes and 3 proposals
009935.jpg: 11 GT bboxes and 21 proposals
009939.jpg: 2 GT bboxes and 6 proposals
009946.jpg: 4 GT bboxes and 4 proposals
009947.jpg: 2 GT bboxes and 4 proposals
009950.jpg: 2 GT bboxes and 2 proposals
009954.jpg: 2 GT bboxes and 7 proposals
009955.jpg: 2 GT bboxes and 6 proposals
009958.jpg: 5 GT bboxes and 30 proposals
Total inference time: 36.4s
15.86% = aeroplane AP 
/content/mAP/main.py:704: MatplotlibDeprecationWarning: 
The set_window_title function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use `.FigureManagerBase.set_window_title` or GUI-specific methods instead.
  fig.canvas.set_window_title('AP ' + class_name)
5.49% = bicycle AP 
10.19% = bird AP 
0.80% = boat AP 
0.42% = bottle AP 
8.10% = bus AP 
9.32% = car AP 
48.89% = cat AP 
0.95% = chair AP 
8.52% = cow AP 
6.79% = diningtable AP 
19.11% = dog AP 
27.72% = horse AP 
14.26% = motorbike AP 
13.19% = person AP 
0.52% = pottedplant AP 
0.98% = sheep AP 
8.70% = sofa AP 
32.28% = train AP 
9.03% = tvmonitor AP 
mAP = 12.06%
/content/mAP/main.py:298: MatplotlibDeprecationWarning: 
The set_window_title function was deprecated in Matplotlib 3.4 and will be removed two minor releases later. Use `.FigureManagerBase.set_window_title` or GUI-specific methods instead.
  fig.canvas.set_window_title(window_title)
Figure(640x480)

import os

os.system('jupyter nbconvert --to html w23_ece495_assignment4.ipynb')

This application is used to convert notebook files (*.ipynb)
        to various other formats.

        WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--show-config
    Show the application's configuration (human-readable format)
    Equivalent to: [--Application.show_config=True]
--show-config-json
    Show the application's configuration (json format)
    Equivalent to: [--Application.show_config_json=True]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
    Equivalent to: [--NbConvertApp.writer_class=StdoutWriter]
--inplace
    Run nbconvert in place, overwriting the existing notebook (only
            relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place,
            overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document.
            This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True --TemplateExporter.exclude_input_prompt=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
    Equivalent to: [--WebPDFExporter.allow_chromium_download=True]
--disable-chromium-sandbox
    Disable chromium security sandbox when converting to PDF..
    Equivalent to: [--WebPDFExporter.disable_sandbox=True]
--show-input
    Shows code input. This flag is only useful for dejavu users.
    Equivalent to: [--TemplateExporter.exclude_input=False]
--embed-images
    Embed the images as base64 dataurls in the output. This flag is only useful for the HTML/WebPDF/Slides exports.
    Equivalent to: [--HTMLExporter.embed_images=True]
--sanitize-html
    Whether the HTML in Markdown cells and cell outputs should be sanitized..
    Equivalent to: [--HTMLExporter.sanitize_html=True]
--log-level=<Enum>
    Set the log level by value or name.
    Choices: any of [0, 10, 20, 30, 40, 50, 'DEBUG', 'INFO', 'WARN', 'ERROR', 'CRITICAL']
    Default: 30
    Equivalent to: [--Application.log_level]
--config=<Unicode>
    Full path of a config file.
    Default: ''
    Equivalent to: [--JupyterApp.config_file]
--to=<Unicode>
    The export format to be used, either one of the built-in formats
            ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf']
            or a dotted object name that represents the import path for an
            ``Exporter`` class
    Default: ''
    Equivalent to: [--NbConvertApp.export_format]
--template=<Unicode>
    Name of the template to use
    Default: ''
    Equivalent to: [--TemplateExporter.template_name]
--template-file=<Unicode>
    Name of the template file to use
    Default: None
    Equivalent to: [--TemplateExporter.template_file]
--theme=<Unicode>
    Template specific theme(e.g. the name of a JupyterLab CSS theme distributed
    as prebuilt extension for the lab template)
    Default: 'light'
    Equivalent to: [--HTMLExporter.theme]
--sanitize_html=<Bool>
    Whether the HTML in Markdown cells and cell outputs should be sanitized.This
    should be set to True by nbviewer or similar tools.
    Default: False
    Equivalent to: [--HTMLExporter.sanitize_html]
--writer=<DottedObjectName>
    Writer class used to write the
                                        results of the conversion
    Default: 'FilesWriter'
    Equivalent to: [--NbConvertApp.writer_class]
--post=<DottedOrNone>
    PostProcessor class used to write the
                                        results of the conversion
    Default: ''
    Equivalent to: [--NbConvertApp.postprocessor_class]
--output=<Unicode>
    Overwrite base name use for output files.
                Supports pattern replacements '{notebook_name}'.
    Default: '{notebook_name}'
    Equivalent to: [--NbConvertApp.output_base]
--output-dir=<Unicode>
    Directory to write output(s) to. Defaults
                                  to output to the directory of each notebook. To recover
                                  previous default behaviour (outputting to the current
                                  working directory) use . as the flag value.
    Default: ''
    Equivalent to: [--FilesWriter.build_directory]
--reveal-prefix=<Unicode>
    The URL prefix for reveal.js (version 3.x).
            This defaults to the reveal CDN, but can be any url pointing to a copy
            of reveal.js.
            For speaker notes to work, this must be a relative path to a local
            copy of reveal.js: e.g., "reveal.js".
            If a relative path is given, it must be a subdirectory of the
            current directory (from which the server is run).
            See the usage documentation
            (https://nbconvert.readthedocs.io/en/latest/usage.html#reveal-js-html-slideshow)
            for more details.
    Default: ''
    Equivalent to: [--SlidesExporter.reveal_url_prefix]
--nbformat=<Enum>
    The nbformat version to write.
            Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
    Equivalent to: [--NotebookExporter.nbformat_version]

Examples
--------

    The simplest way to use nbconvert is

            > jupyter nbconvert mynotebook.ipynb --to html

            Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'qtpdf', 'qtpng', 'rst', 'script', 'slides', 'webpdf'].

            > jupyter nbconvert --to latex mynotebook.ipynb

            Both HTML and LaTeX support multiple output templates. LaTeX includes
            'base', 'article' and 'report'.  HTML includes 'basic', 'lab' and
            'classic'. You can specify the flavor of the format used.

            > jupyter nbconvert --to html --template lab mynotebook.ipynb

            You can also pipe the output to stdout, rather than a file

            > jupyter nbconvert mynotebook.ipynb --stdout

            PDF is generated via latex

            > jupyter nbconvert mynotebook.ipynb --to pdf

            You can get (and serve) a Reveal.js-powered slideshow

            > jupyter nbconvert myslides.ipynb --to slides --post serve

            Multiple notebooks can be given at the command line in a couple of
            different ways:

            > jupyter nbconvert notebook*.ipynb
            > jupyter nbconvert notebook1.ipynb notebook2.ipynb

            or you can specify the notebooks list in a config file, containing::

                c.NbConvertApp.notebooks = ["my_notebook.ipynb"]

            > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.

[NbConvertApp] WARNING | pattern 'w23_ece495_assignment4.ipynb' matched no files

65280

EECS 498-007/598-005 Assignment 5-1: Single-Stage Object Detector - YOLO¶

Single-Stage Object Detector¶

Getting Started¶

Install starter code¶

Setup code¶

Load PASCAL VOC 2007 data¶

Coordinate transformation¶

Data Visualizer¶

Visualize PASCAL VOC 2007¶

Detector Backbone Network¶

Anchor and Proposal¶

Anchor shapes¶

Activation Grid Generator¶

Anchor Generator¶

Proposal Generator¶

YOLO¶

Faster R-CNN¶

Training¶

Prediction Networks¶

Intersection Over Union (IoU)¶

Activated (positive) and negative anchors¶

Prediction Network¶

Loss Function¶

Confidence score regression¶

Bounding box regression¶

Object classifiction¶

Train an object detector¶

Object detection module¶

Object detection solver¶

Overfit small data¶

Train a net¶

Use an object detector¶

Thresholding¶

Non-Maximum Suppression (NMS)¶

Inference¶

Inference - overfit small data¶

Inference - train a net¶

Evaluation¶